Advances in Multimedia Information Processing -- PCM 2010, - download pdf or read online

By Guoping Qiu, Kin Man Lam, Hitoshi Kiya, Xiang-Yang Xue, C.-C. Jay Kuo, Michael S. Lew

ISBN-10: 3642156959

ISBN-13: 9783642156953

This ebook constitutes the lawsuits of the eleventh Pacific Rim convention on Advances in Multimedia details Processing, held in Shanghai in September 2010.

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Read Online or Download Advances in Multimedia Information Processing -- PCM 2010, Part II: 11th Pacific Rim Conference on Multimedia, Shanghai, China, September 21-24, 2010 Proceedings PDF

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Extra resources for Advances in Multimedia Information Processing -- PCM 2010, Part II: 11th Pacific Rim Conference on Multimedia, Shanghai, China, September 21-24, 2010 Proceedings

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The most straightforward approach to performing multi-label annotation is to construct a G. Qiu et al. ): PCM 2010, Part II, LNCS 6298, pp. 22–30, 2010. c Springer-Verlag Berlin Heidelberg 2010 Image Annotation by Sparse Logistic Regression 23 binary classifier for each label separately using the one-against-the-rest scheme [13]. In this approach, instances relevant to each given label form the positive class, and the rest form the negative class. Since the labels of images are not independent of each other but actually often have significant correlations with each other,multi-label learning is studied recently in order to explicitly take advantage of the correlations between multiple tags during image annotation.

Various multidimensional indexing structures have been developed to improve scalability of retrieval process over large scale multimedia data collection. In general, the existing approaches can be classified into three broad categories including (1) tree indexing structures, (2) data approximation, and (3) dimensionality reduction [6]. In this article, we mainly focus on tree based indexing structure. Existing data structure in this class can be further split into two substreams. They are Data Partitioning-based (DP) and Spatial Partition-based (SP).

Currently, Query Independent Fusion (QIF) is the most efficient and naive approach [22, 23]. It applies the same search mechanism to any kind of incoming queries. Its main advantages include simplicity and robustness. However, search engines based on QIF generally suffer from low query accuracy and poor scalability to cover complexity of possible queries. The expectation for query fusion is that query constructed with combining various modalities is more informative and completed than the ones based on single input.

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Advances in Multimedia Information Processing -- PCM 2010, Part II: 11th Pacific Rim Conference on Multimedia, Shanghai, China, September 21-24, 2010 Proceedings by Guoping Qiu, Kin Man Lam, Hitoshi Kiya, Xiang-Yang Xue, C.-C. Jay Kuo, Michael S. Lew


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