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Here are the refereed proceedings of the 5th International Conference on Image and Video Retrieval, CIVR 2006, held in Singapore in July 2006. Presents 18 revised full papers and 30 poster papers, together with extended abstracts of 5 papers of 1 special session and those of 10 demonstration papers. These cover interactive image and video retrieval, semantic image retrieval, visual feature analysis, learning and classification, image and video retrieval metrics, and machine tagging.
It was our great pleasure to host the 4th International Conference on Image and Video Retrieval (CIVR) at the National University of Singapore on 20–22 July 2005. CIVR aims to provide an international forum for the discussion of research challenges and exchange of ideas among researchers and practitioners in image/video retrieval technologies. It addresses innovative research in the broad ?eld of image and video retrieval. A unique feature of this conference is the high level of participation by researchers from both academia and industry. Another unique feature of CIVR this year was in its format – it o?ered both the traditional oral presentation sessions, as well as the short presentat...
First of all, we appreciate the hard work of all the authors who contributed to ICEC 2005 by submitting their papers. ICEC 2005 attracted 95 technical paper submissions, 8 poster submissions and 7 demo submissions, in total 110. This number is nearly equal to ICEC 2004. Based on a thorough review and selection process carried out by 76 international experts from academia and industry as members of the senior and international program committees, a high-quality program was compiled. The program committee consisted of experts from all over the world: 1 from Austria, 3 from Bulgaria, 2 from Canada, 4 from China, 1 from Finland, 4 from France, 10 from Germany, 1 from Greece, 1 from Ireland, 1 fr...
Solutions for learning from large scale datasets, including kernel learning algorithms that scale linearly with the volume of the data and experiments carried out on realistically large datasets. Pervasive and networked computers have dramatically reduced the cost of collecting and distributing large datasets. In this context, machine learning algorithms that scale poorly could simply become irrelevant. We need learning algorithms that scale linearly with the volume of the data while maintaining enough statistical efficiency to outperform algorithms that simply process a random subset of the data. This volume offers researchers and engineers practical solutions for learning from large scale ...
This dictionary is intended for anyone who is interested in translation and translation technology. Especially, translation as an academic discipline, a language activity, a specialized profession, or a business undertaking. The book covers theory and practice of translation and interpretation in a number of areas. Addressing and explaining important concepts in computer translation, computer-aided translation, and translation tools. Most popular and commercially available translation software are included along with their website addresses for handy reference. This dictionary has 1,377 entries. The entries are alphabetized and defined in a simple and concise manner.
Multimedia is changing the design of database and information retrieval systems. The accumulation of audio, image, and video content is of little use in these systems if the content cannot be retrieved on demand, a critical requirement that has led to the development of new technologies for the analysis and indexing of media data. In turn, these technologies seek to derive information or features from a data type that can facilitate rapid retrieval, efficient compression, and logical presentation of the data. Significant work that has not been addressed, however, is the benefits of analyzing more than one data type simultaneously. Computed Synchronization for Multimedia Applications presents...
With the explosion of video and image data available on the Internet, desktops and mobile devices, multimedia search has gained immense importance. Moreover, mining semantics and other useful information from large-scale multimedia data to facilitate online and local multimedia content analysis, search, and other related applications has also gained an increasing attention from the academia and industry. The rapid increase of multimedia data has brought new challenges to multimedia content analysis and multimedia retrieval, especially in terms of scalability. While on the other hand, large-scale multimedia data has also provided new opportunities to address these challenges and other convent...
Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field.
Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances in hardware and software technology have lead to a number of unique scenarios where text mining algorithms are learned. Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining. This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a s...
Traditionally, scientific fields have defined boundaries, and scientists work on research problems within those boundaries. However, from time to time those boundaries get shifted or blurred to evolve new fields. For instance, the original goal of computer vision was to understand a single image of a scene, by identifying objects, their structure, and spatial arrangements. This has been referred to as image understanding. Recently, computer vision has gradually been making the transition away from understanding single images to analyzing image sequences, or video Video understanding deals with understanding of video understanding. sequences, e.g., recognition of gestures, activities, facial ...