Content-based Image Retrieval (CBIR) System

Content-based Image Retrieval (CBIR) System Chapter 1. Introduction Nowadays, in the most of areas it is necessary to work with large amounts of growing visual and multimedia data, at the same time, the number of image and video files on the web is quite big and is still rising very rapidly. Searching through this data is absolutely vital. So, there is a high demand on the tools for image retrieving, which are based on visual information, rather than simple text-based queries. Content-based Image Retrieval (CBIR) consists of retrieving the most visually similar images to a given query image from a database or group of image files. It is a quite useful thing in a lot of areas such as Photography which may involve image search from the large digital photo galleries; Medicine it is used to assist in diagnosis. In most of diseases, their visual characteristics carry diagnostic information and visually similar images correspond to the same disease category. The output of a CBIR system can help to make a decision (Tahmoush, 2007); Military detection of e nemy soldiers or vehicles from screen photographs; Crime prevention it helps police in suspicious peoples identification from large image databases and in image retrieval of crime scene photos (Wen, 2005); Geography frequently used in Geographical information systems (GIS) (Hafiane, 2006) and many others. CBIR has been a subject of intense research over the last 15 years. It is one of the most difficult research areas in multimedia computing and information retrieval. During the research history many different image matching, indexing and retrieval algorithms have been tried. Practice shows that user queries described by visual information are more effective and more precisely meet user needs, than standard text search queries. It is because visual information is closer to the humans perception of the world. 1.1 CBIR Systems Many CBIR systems and tools have been developed to make queries based on visual content. During the 90-ies several notable commercial systems were introduced. IBM developed Query By Image Content (QBIC) system, which lets user to make queries of large image databases based on visual image content properties such as Example images; User-constructed sketches and drawings; Selected color and texture patterns. (Flickner, 1995) Soon after that â€Å"Virage Image Search Engine† of Virage Inc. was developed, which provides an open framework for building systems that explicitly manages image assets by directly representing their visual attributes. (Bach, 1996) Several online content-based web search engines can also be mentioned. â€Å"WebSEEk† developed by Image and Advanced Television Lab, Columbia University. It allows making queries by example and by desired color composition. â€Å"Chabot†, Developed by Department of Computer Science, University of California, which allows to search by colors, but offers limited options such as choosing one dominant color. (Veltkamp, 2002) Global Memory Net (GMNet) was launched for public access in late June 2006. It is a digital library of cultural, historical, and heritage image collections. Among other text-based searching types this web library has a possibility to search by image content. It has two basic options for content based searching. Search by example image, based on its color and shape and by user drawing. For CBIR, GMNet uses SIMPLIcity developed by Prof. James Z. Wang of Penn State University. (Chen 2006) Different CBIR systems use different types of user queries. Typically tools for the content-based image retrieval consist of query statement and a result presentation; this query can be done by providing an example image a sketch, or by choosing desired colors for the image. Results are presented by the top several similar images based on the similarity measure. 1.2 Research Questions Despite the large number of CBIR systems developed, there are still a lot of challenging problems in this area. The important sides that still need to be improved are speed of retrieving, when working with the large databases, accuracy and effectiveness of the retrieved results. So the researchers from multiple disciplines are deeply concerned with these aspects. Comparisons by image content are much more complicated task than by textual data. Generally, content-based image retrievals are based on comparison of image content descriptors that represent visual features of the image. Different features can be used to obtain the image descriptor. To meet specific user needs and in various cases some of them are more effective than others. Sometimes the implementation simplicity is as important as retrieval accuracy and effectiveness. Based on the previous discussion, research questions are the following: What are the basic retrieval techniques? What kind of features are usually used? How the features are obtained from the image? How these features are matched? How the retrieval results are presented to the user? How accurate can be the algorithms, which are relatively easy to implement? 1.3 Objectives The CBIR research often involves two areas computer vision and database systems. The database systems part studies database indexing, searching and retrieval techniques and computer vision part is about image processing, obtaining the image descriptors and image matching. In order to answer the research questions this dissertation focuses on a computer vision part. Image processing and image transformations are used by CBIR systems in order to extract image descriptors. CBIR systems are based on different image features descriptors matching. Some of these systems perform image comparison by multiple features at the same time and some of them use only one feature. In this dissertation we are going to investigate what are the basic techniques used in CBIR systems, which are based on different feature descriptors. We will make a detailed overview of these basic methods. We are also going to implement one of the most effective algorithms in the CBIR field. This is Scale Invariant Feature Transform (SIFT) algorithm (Lowe, 2004) and see how effective and accurate it can be. Chapter 2. Literature Survey 2.1 CBIR systems typical architecture Typical CBIR system has two main functionalities. This is Data insertion and query processing. Data insertion procedures are performed independent of user interaction.   They are applied to all the data. The purpose of this process is to extract visual features from the images in the database. These features are obviously smaller than the actual image and they are then stored for easy comparison reasons, as a characterizers of each image. Query processing starts with user specific request. Request can be done in several ways: By an example image, by giving desired pattern or object, color distribution and etc. Query processing module obtains the visual features from the given request, metric is defined. Then similarity is measured based on the chosen metric and some set of the most similar images are . Features extraction itself involves, selecting the features that have to be extracted, it depends on the type of user query. The feature extracting algorithm is chosen to create the feature vector from the selected features. Eventually, image descriptor is formed which are then used to compare the images. (Torres, 2006) 2.2 Semantic Gap Basically, similarity searching between the images is based on low-level and higher-levels of queries. (Eakins, 1996) Low-Level Similarity in this case visual features to describe the image are primitives such as color, texture and shape. Higher-Levels, Semantic Similarity at higher levels, similarity searching is not based on a simple features. In this case images are described by higher level of semantic attributes. This involves identification of the object types depicted in the image. These two levels of queries form the problem called semantic gap. Semantic gap can be defined in the following way: â€Å"The semantic gap is the lack of coincidence between the information that one can extract from the visual data and the interpretation that the same data has for a user in a given situation.† (Datta, 2008) In another words, images with high low-level feature similarities may still be different in terms of user perception. So similarity by low-level features, not always mean semantic similarity of these images. 2.3 Content Comparison Techniques This dissertation is concerned with low-level similarity features extraction .CBIR for low-level similarity queries needs techniques which can be used to obtain the image content descriptors to compare images based on their color, texture and shape. Color Image content comparison by color is based on matching images by their color distribution. In this case image feature identifies the proportion of pixels of specific color or colors within an image. So one can make color searches by indicating desired concentration of colors or by an example image with desired color distribution and get similar images. Color histograms are widely used to extract the color distribution descriptors from the image. It is a statistic of the color of pixels in the image. First color distribution is represented by appropriate color histogram, and then color vector is formed from that histogram. Lets discuss several color feature extraction histograms. Conventional Color Histogram (CCH) This histogram consists of occurrences of each color in the image. Each pixel is associated to only one its own histogram bin only on the basis of its own color. This color histogram uses the probability mass function of the image pixel intensities. (Suhasini, 2009) Fuzzy Color Histogram (FCH) as an opposite to CCH, in FCH each pixel is associated to all bins of histogram with different degrees of membership depending on color similarity of the pixel. This is done by fuzzy-set membership function. (ferone, 2008) Color Correlogram (CC) color correlogram of an image is a table which is indexed by color pairs, where the d-th entry of (i,j) cell shows the probability of finding the color j at a distance of d from a pixel of color i in the image extracting. Such a feature from the image is tolerant to the changes in appearance of the same scene which can be caused by changing the viewing positions, but color correlogram is more difficult to compute than color histograms. (Huang, 1997) Texture Retrieval by image texture in a similar to color-based feature extraction, but it looks for visual patterns in images rather than colors. So it looks at homogeneity that is not a result of a single color presence or intensity of a pixel value. Sometimes it also provides more spatial information. The most basic method used to extract the texture descriptor from the image is based on Fourier Transform. The initial image is transformed by the Fourier function. As the method works on digital images, Discrete Fourier Transform (DFT) is used. DFT converts images from the spatial domain into the frequency domain, where all the spatial frequencies of the original image are represented. In another words this transformed image shows intensity variations over a number of pixels. Transformed data is grouped to obtain several measures from it. Then descriptor is formed of these measures and is used for comparison. (Nixon, 2007) Shape Shape-based image retrieval comparison looks at shapes of regions within an image and searches for the shapes similar to given as in a query image. Edge and blob detections are important parts for the shape feature extraction. These edges and blobs are points or regions in the image that are either brighter or darker than the surrounding. Several methods are used for shape-based image retrieval, which involve different kind of image filtering and image transformations. One of the most effective algorithms for shape-based image retrieval is Scale Invariant Feature Transform (SIFT) algorithm, which was first developed by David Lowe in 1999, at the University of British Colombia. It takes a single image as an input and returns a set of detected image features. In SIFT algorithm image filtering is based on Gaussian function. After image filtering SIFT uses Difference of Gaussian (DoG) pyramid for blob (keypoint) detection. The image feature descriptor, which is called keypoint descriptor is 128 element feature vector and formed of gradient magnitudes and orientations computed for the area around the identified keypoints. (Lowe, 2004) Chapter 3. Research Method 3.1 Research approach Mathematical methods play key role in the most of CBIR algorithms. Often mathematical solution of the problem is difficult or impossible to implement practically, therefore it is important to assess the method in practice. Thats why Experimental approach will be used in this dissertation. This method of primary research forces to experience and overcome all the difficulties that can appear during the practical implementation of theory. It requires focusing on the details of algorithm and clearly shows advantages and disadvantages of the particular algorithm. It also gives possibility to assess the instruments used in experiment, which are not less important than algorithm itself. In this dissertation, one of the CBIR algorithms for shape-based image retrieval will be implemented for a number of images and the results will be assessed 3.2 Tools and Technologies used This study focuses on the algorithm which involves image processing. It will be implemented under the Microsoft .net framework platform and using GDI+ and C# programming language. .Net framework provides managed interface for GDI+; therefore its relatively easy to process images using this platform. Microsoft Visual Studio .Net will be used as an IDE. This experiment will also show how useful can be .net framework library and C# language for image processing purpose. References: Bach J., Fuler C., Gupta A., Hampapur A., Horowitz B., Humphrey R., Jain R., Shu C., (1996) The virage image search engine: An open framework for image management SPIE Conference on Storage and Retrieval for Image and Video Databases; Chen Ch. Ch. (2006),Using Tomorrows Retrieval Technology to Explore the Heritage: Bonding Past and Future in the Case of Global Memory Net; available at: http://ifla.queenslibrary.org/IV/ifla72/papers/097-Chen-en.pdf last accessed on 24th September 2009 Datta R., Joshi D., Li J. and Wang J. Z. (2008) Image Retrieval: Ideas, Influences, and Trends of the New Age. Eakins J.P. (1996) Automatic image content retrieval are we getting anywhere?Department of Computing, University of Northumbria at Newcastle, available at: http://www.cs.uu.nl/docs/vakken/mir/materials/literature/eakins.pdf last accessed on 24th September 2009 Ferone A., Maddalena L., Petrosino A., (2008) The Enhanced Color Histogram: a way for dealing with uncertainty in CBIR systems, University of Naples Parthenope, Department of Applied Science; Flickner M., Sawhney H., Niblack W., Ashley J., Huang Q., Dom B., Gorkani M., Hafher J., Lee D., Petkovie D., Steele D. and Yanker P.(1995) Query by Image and Video Content: The QBIC System, IBM Almaden Research Center; available at: http://www2.cs.ucy.ac.cy/~nicolast/courses/cs422/ReadingProjects/qbic.pdf last accessed on 24th September 2009; Hafiane A., Chaudhuri S., Seetharaman G., Zavidovique B. (2006) Region-based CBIR in GIS with local space filling curves to spatial representation Huang J., Kumar S. R., Mitra M., Zhu W. J., Zabih R. (1997) Image Indexing Using Color Correlograms, Cornell University; Lowe D. G. (2004), Distinctive Image Features from Scale-Invariant Keypoints, Computer Science Department University of British Columbia; available at: http://people.cs.ubc.ca/~lowe/papers/ijcv04.pdf last accessed on 24th September 2009 Nixon M. S., Aguado A. S. (2007) Feature Extraction and Image Processing, Academic Press; Suhasini P.S., Dr. K. Sri Rama Krishna, Dr. I. V. Murali Krishna (2009) CBIR Using Color Histogram Processing; VR Siddhartha Engineering College; available at: http://www.jatit.org/volumes/research-papers/Vol6No1/13Vol6No1.pdf last accessed on 24th September 2009; Tahmoush D.   Hanan S. (2007)A Web Collaboration System for Content-Based Image Retrieval of Medical imag;available at:http://www.cs.umd.edu/~hjs/pubs/medicalimagepapers/TahmoushSPIE07a.pdf last accessed on 24th September 2009; Torres R. S., Falcà £o A. X. (2006)Content-Based Image Retrieval: Theory and Applications; available at: http://www.dcc.unicamp.br/~rtorres/artigos/journal/torres06rita.pdf last accessed on 24th September 2009; Veltkamp R. C., Tanase M. (2002) Content-Based Image Retrieval Systems: A Survey; Department of Computing Science, Utrecht University; available at: http://give-lab.cs.uu.nl/cbirsurvey/cbir-survey.pdf last accessed on 24th September 2009; Wang J. Z. (2001) SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture Libraries; available at: http://infolab.stanford.edu/~wangz/project/imsearch/SIMPLIcity/TPAMI/wang2.pdf last accessed on 24th September 2009; Wen Ch. Y, Yu Ch. Y., (2005) Image Retrieval of Digital Crime Scene Images, Forensic Science Journal; available at: http://fsjournal.cpu.edu.tw/content/vol4.no.1/06-95-04.pdf last accessed on 24th September 2009.

Essay --

Forensic science is misleading because it suggests only one type of science is involved, but this is not the case. Forensic investigations can involve virtually any field of science and technology. There are many steps that have to be taken when leading a criminal investigation and investigating a crime scene. Firstly, detectives have to try and figure out why and how a crime was committed. They examine a crime scene looking for information or clues such as fingerprints, weapons, and DNA. They investigate the victims’ history to define why someone would want to harm them. After they have formed a hypothesis, they try to find proof that somebody committed a crime so that they can arrest the suspects. They look at both the cause and the actual evidence of the crime and try to see if their hypothesis makes sense. The suspects then enter the criminal justice system where they are tried using the evidence collected at the crime scene. Forensic investigations require skills of specially trained scientists, police, engineers, doctors and others. â€Å"These investigators observe all types of evidence, from weapons to bloodstains and from computers to bugs† (Erzinclioglu 5). The greater the evidence against a person, the greater the chance of conviction. Homicide detectives usually work in pairs and approach a crime scene with five basic questions: Did the death take place at the crime scene or elsewhere? Was there any attempt to alter the crime scene? Does the scene point to any particular activity such as drug abuse or burglary? Is the cause of death obvious? Are there sufficient clues pointing to how the death occurred? â€Å"Scene processing is the term practical to the series of steps taken to investigate a crime scene. Although the methods an... ...rams that improve and image by enhancing the contrast, which is the difference in color concentrations. Changing the brightness or dullness of the image. Increasing the resolution and sharpening or de- blurring the image. Fingerprints are a guaranteed method of identification because each person’s fingertips are unique and fingerprints don’t change throughout a person’s lifetime. Fingerprint ridges appear in three pattern types; loop, whorls, and arches. The most collective type of fingerprint pattern, loops begin at one side of the fingertip, double back, and exit on the same side. Whorls are the second most common type of fingerprint pattern. They form a circular pattern which sometimes look like a bull’s-eye on a dartboard. Arches are less common than loops and whorls. They form a wavelike pattern which begins on one side of the fingertip and ends on the other. Essay -- Forensic science is misleading because it suggests only one type of science is involved, but this is not the case. Forensic investigations can involve virtually any field of science and technology. There are many steps that have to be taken when leading a criminal investigation and investigating a crime scene. Firstly, detectives have to try and figure out why and how a crime was committed. They examine a crime scene looking for information or clues such as fingerprints, weapons, and DNA. They investigate the victims’ history to define why someone would want to harm them. After they have formed a hypothesis, they try to find proof that somebody committed a crime so that they can arrest the suspects. They look at both the cause and the actual evidence of the crime and try to see if their hypothesis makes sense. The suspects then enter the criminal justice system where they are tried using the evidence collected at the crime scene. Forensic investigations require skills of specially trained scientists, police, engineers, doctors and others. â€Å"These investigators observe all types of evidence, from weapons to bloodstains and from computers to bugs† (Erzinclioglu 5). The greater the evidence against a person, the greater the chance of conviction. Homicide detectives usually work in pairs and approach a crime scene with five basic questions: Did the death take place at the crime scene or elsewhere? Was there any attempt to alter the crime scene? Does the scene point to any particular activity such as drug abuse or burglary? Is the cause of death obvious? Are there sufficient clues pointing to how the death occurred? â€Å"Scene processing is the term practical to the series of steps taken to investigate a crime scene. Although the methods an... ...rams that improve and image by enhancing the contrast, which is the difference in color concentrations. Changing the brightness or dullness of the image. Increasing the resolution and sharpening or de- blurring the image. Fingerprints are a guaranteed method of identification because each person’s fingertips are unique and fingerprints don’t change throughout a person’s lifetime. Fingerprint ridges appear in three pattern types; loop, whorls, and arches. The most collective type of fingerprint pattern, loops begin at one side of the fingertip, double back, and exit on the same side. Whorls are the second most common type of fingerprint pattern. They form a circular pattern which sometimes look like a bull’s-eye on a dartboard. Arches are less common than loops and whorls. They form a wavelike pattern which begins on one side of the fingertip and ends on the other.

Bad law Essay

Bad law BY erik2408 Bad Law/Lawyers Introduction: Law is a term which does not have a universally accepted definition, but one definition is that law is a system of rules and guidelines which are enforced through social institutions to govern behavior. What is bad law you may ask? Bad law ties in with quite a few things, as a matter of fact it’s a very big subject but I would like to break it down to Just a few certain topics. The topics that I would like to discuss are what is and makes a bad lawyer in general, the morality of some lawyers. You could be a great lawyer but if you have horrible morals by my definition I would have to abel you as a bad lawyer. But my most important topic is on lawyers and ambulance chasing which is frowned upon. There are two great movies that explain both of these topics. One of the movies is â€Å"My cousin Vinny’ which shows you an example of an overall bad lawyer. The other movie is a bit more popular â€Å"The Verdict† which portrays a morally bad lawyer that is an ambulance chaser. Discussion: Let’s get started! The first thing I’d like to discuss is a bad lawyer Just in general. I don’t know if a lot of people seen the movie â€Å"My Cousin Vinny’ but that movie is a great example of a bad lawyer. For the people that are unfamiliar with it let me tell you a little bit about it. The movie starts off by two boys driving across the country and get arrested and trailed for murder, for the killing of the clerk at a gas station they had Just been into. The two boys were innocent and but there were witnesses saying they saw them. They were broke and in big trouble. So one of the boys called his uncle Vinny who was a lawyer. This was Vinnie’s first case ever as a lawyer and was completely clueless. Vinny did not know the rules and regulations on how to behave in a courtroom to say the least. Somehow he convinced the kids to let him represent them. Vinny was learning as we went along, and with the help of his girlfriend he actually won the case. But if I was being trailed for murder I for sure know I would not want him representing me. If you are in need of a lawyer here’s some tips you may need to know to avoid the bad ones. For one don’t go for a general practitioner when you need a specialist. Two, do some legwork to find a good attorney. Three, don’t sign up unless you’re completely comfortable with the fee arrangement and relationship. Four, understand what an attorney is doing for you. Last but not least be wary of the â€Å"slam dunk† claim. Any lawyer who says you got a slam dunk case is not a very good lawyer (Collins). Hopefully that information gives you a better understanding ofa bad lawyer. Now to talk about my main issue, which are lawyers with bad morals. What do I consider a morally bad lawyer? An attorney that is an ambulance chaser would be considered a morally bad lawyer in my eyes. An ambulance chasing is a form of barratry, refers to a lawyer using an event as a lawyers that follow ambulances to the emergency room to find clients. Ambulance chasing is prohibited in the US. Such conduct violates Rule 7. 3 of the American Bar Association Model Rules of Professional Conduct. Some bar associations strongly enforce rules against barratry. For example, the State Bar of California dispatches investigators to large-scale disaster scenes to discourage ambulance chasers, and to catch any who attempt to solicit business from disaster victims at the scene. Ambulance chasing is also illegal in Australia, in accordance with clauses 20 and 22 of the Legal Profession Regulation of 1987 (Wikipedia). Lawyers aren’t supposed to solicit their clients directly, at least in theory (Toothman). But of course that would be in a perfect world, but as right now the reality is that there are always some lawyers cutting deals with ambulance drivers, nurses, and funeral homes so on and so forth just to get tips on finding victims that might have a legal case (Toothman). When there’s a big event, like an explosion or a train wreck or even a bus crash, how do the random victims find their way to the hungry lawyers? In the good old days, plaintiff lawyers chartered planes to fly to Bhopal, India, for example (Toothman). In Toothman’s article he writes about Lawyers fake press release to sign up clients. What these lawyers did was send out a bizarre press release, announcing that, without even having a single client so far, they are â€Å"investigating† or â€Å"looking into† the disaster du Jour, which they have ripped from the headlines (Toothman). Du Jour is a magazine by the way. That wasn’t intended to get a local paper to run a story about that, the goal was to reach out to any victims or their families who might be looking around for hope, or a lawyer, will stumble upon these press releases using Google or yahoo and rush to sign up with the hungry lawyer (Toothman). In other recent rticles about ambulance chasing an attorney in Texas was arrested for doing this. The event took place in May of 2012 only a year and half ago. Ronald Reynolds was a state representative and former municipal Judge. He was arrested for barratry which is a fancier term for ambulance chasing. Texas, it’s not Just prohibited by legal ethics rules it’s also flat out illegal (Rabiner). This was such a widespread problem at the Harris County Courthouse near Houston, officials recently erected signs warning attorneys that barratry is a crime (Rabiner). The legislator was caught soliciting otential clients soon after they had been in car accidents. It was also reported that he founds some clients with the help of a local chiropractor. This is something you would not expect from a state representative. It’s very sketchy and illegal in the state of Texas which I did not know until reading this article. In Texas, attorneys arent permitted to send unsolicited offers of representation until 31 days after the underlying incident. Or when they know the individual already has legal representation (Rabiner). In addition to breaking these rules, the District Attorney has also accused Rep. Reynolds of tricking an undercover investigator he never met into signing an attorney-client agreement that gave him part of any settlement. â€Å"Any settlement† included insurance monies (Rabiner). What I found to be humorous in the article was the Rep. Ronald Reynolds even voted for the barratry law back in 2011. You would think someone who votes for a certain law would not be dumb enough to break that very law. Especially someone with a stature of being a state representative. I was appalled and in shock after reading this how can somebody be I was unaware that ambulance chasing was so common, I ran into another article here a lawyer was accused of offering kickbacks to two local insurance agents for referring accident cases to him, challenging the legality of the state statute that makes such practices a crime (Smith). Scott Jeffrey Jontiff, 33, of Miami, was charged with two counts of unlawful solicitation by an attorney after insurance agents he contacted turned him in to the Florida Division of Insurance Fraud in West Palm Beach (Smith). It was stated in the police report that there was a recorded and videotaped conversation where Jontiff offered to give the insurance agents $100 for ach personal injury case referred to him or $1 50 if the agents waited until the insurance companies paid up (Smith). The charges that Scott Jeffery faced were 3rd degree felony which is punishable by 5 years in prison and 5000 dollar fine. What I don’t understand is why put yourself in such danger by committing such a crime to me it seems stupid. You’re eventually going to get caught sooner or later. To get a better understanding on ambulance chasing and actually have a visual aid I strongly recommend watching the movie â€Å"The Verdict†. â€Å"The Verdict† came out in 1982 it’s an ld movie but very popular back in the day and still is, and yes it’s in color. The movie portrays a lawyer who is down-on-his luck, reduced to drinking and ambulance chasing. The down on his luck lawyer is Frank Galvin. Former associate Mickey Morrissey reminds him of his obligations in a medical malpractice suit that he himself served to Galvin on a silver platter (The Verdict). All parties were willing to settle out of court, until Frank suddenly realized that perhaps after all the case should go to court, to punish the guilty and get a decent settlement for his clients, nd to restore his standing as a lawyer (The Verdict). Conclusion: Bad lawyers are not Just in movies are not Just in movies. As you can see by the few example articles that I talked about ambulance chasing is a real thing and happens more often than you think. It practically happens on a daily basis there are always lawyers or somebody out there trying to take advantage of you. As I read in a few articles ambulance chasing attorneys are viewed as bottom chasers, theyre not very well liked. â€Å"The Verdict† does a really good Job portraying an ambulance chasing attorney. Those types of lawyers are always trying to get a lawsuit on something to get an easy buck. When you go as far as go to funerals and such right after a family has had a death in the family to try to catch a case in my eyes that’s having low morals and its frowned upon. As far as having a bad lawyer in general you Just have to be careful on that and take all the necessary precautions to avoid them. The tips I stated above are a few helpful hints to spot a bad attorney. As society grows and changes I hope lawyers won’t be as hungry and try to have a little sympathy for others and reevaluate their morals.

My Intercultural Communication Skills Of Multicultural...

There can be many things that we like or dislike about our childhood. Some of the things or even people can still give us negative feelings after so many years. On the other hand, for some of us, there are aspects of childhood that can truly be considered as a gift. In my case, that gift was the environment where I grew up. Needless to say that living as an Armenian immigrant in Russia with a European education was very rough at times. However, the inborn education and skills of multicultural communications were priceless for me. Due to my diverse background, I was able to build a very important ability, which is the intercultural communication competence. This capacity helps one to correctly interact with people of different groups, races, sexual orientation, ethnicities, religions, and so on. 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With the international trade growing and globalisation increasing rapidly, one of the main challenges that organisation encounters is how to increase and manage workforce diversity. Majority of the organisations depend on teams that have different attributes to each other. Businesses utilize the deeper knowledge of multicultural teams, and their variety of skills that will take the organisation forwardRead MoreExploring Gottman Method Therapy And Cognitive Behavioral Individual Therapy1483 Words   |  6 Pagesof both theories, one can integrate and adapt case conceptualization to meet the needs of diverse clients. For this paper, I will be examining the diversity within couple s and families through the lens of intercultural marriage. Biever, Bobele and North (1998) stated that the term intercultural represents a broader range of cultural variables on which couples differ, in particular differences within race, ethnicity and/or religion. For this paper, the specific diversity emphasis is with two clientsRead MoreIntercultural Communication And The Contemporary World Essay2110 Words   |  9 PagesThe rapid development of communications and transportation technologies has gradually transformed human society into a global community in the 21st century, in which the concept of intercultural communication has become a necessity for people to interact with others in an appropriate way to convey effective messages (Chen et al., 2014 p. 1). In order to establish a multicultural civil society across nations and regions, the study of intercultural communication is of great significance for developing