Foundations of Large-Scale Multimedia Information Management by Edward Y. Chang

By Edward Y. Chang

"Foundations of Large-Scale Multimedia details administration and Retrieval: arithmetic of Perception"covers wisdom illustration and semantic research of multimedia facts and scalability in sign extraction, info mining, and indexing. The booklet is split into components: half I - wisdom illustration and Semantic research specializes in the main elements of arithmetic of notion because it applies to info administration and retrieval. those contain characteristic selection/reduction, wisdom illustration, semantic research, distance functionality formula for measuring similarity, and multimodal fusion. half II - Scalability matters provides indexing and allotted tools for scaling up those elements for high-dimensional information and Web-scale datasets. The booklet offers a few real-world functions and comments on destiny learn and improvement instructions.

The booklet is designed for researchers, graduate scholars, and practitioners within the fields of laptop imaginative and prescient, desktop studying, Large-scale facts Mining, Database, and Multimedia info Retrieval.

Dr. Edward Y. Chang was once a professor on the division of electric & desktop Engineering, college of California at Santa Barbara, sooner than he joined Google as a examine director in 2006. Dr. Chang bought his M.S. measure in desktop technology and Ph.D measure in electric Engineering, either from Stanford University.

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Foundations of Large-Scale Multimedia Information Management and Retrieval: Mathematics of Perception

"Foundations of Large-Scale Multimedia info administration and Retrieval: arithmetic of Perception"covers wisdom illustration and semantic research of multimedia facts and scalability in sign extraction, information mining, and indexing. The booklet is split into components: half I - wisdom illustration and Semantic research specializes in the main parts of arithmetic of conception because it applies to information administration and retrieval.

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Given a model, can the availability of more training data improve feature quality, and hence improve annotation accuracy? 2. Can an improve model help data-driven? Give some fixed amount of data, can a model be enhanced to improve feature quality, and hence improve annotation accuracy? We first closely examine a model-based deep-learning scheme, which is neuroscience-motivated. Strongly motivated by the fact that the human visual system can effortlessly conduct these tasks, neuroscientists have been developing vision models based on physiological evidences.

A wide variety of texture analysis methods have been proposed in the past. We chose a discrete wavelet transformation (DWT) using quadrature mirror filters [31] because of its computational efficiency. Each wavelet decomposition on a 2D image yields four subimages: a 12 × 12 scaleddown image of the input image and its wavelets in three orientations: horizontal, vertical and diagonal. Decomposing the scaled-down image further, we obtain a tree-structured or wavelet packet decomposition. The wavelet image decomposition provides a representation that is easy to interpret.

In this chapter, we study two extreme approaches of feature extraction, model-based and data-driven, and then evaluate a hybrid scheme. One may consider model-based and data-driven to be two mutually exclusive approaches. In practice; however, they are not. Virtually all model construction relies on some information from data; all data-driven schemes are built upon some models, † © ACM, 2010. This chapter is a minor revision of the author’s work with Zhiyu Wang and Dingyin Xia [1] published in VLS-MCMR’10.

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