Machine learning approaches for visual information retrieval
In this chapter, we first describe the main stages for deriving image representation from visual local descriptors which has been described in Chapter 2. Coding and pooling steps are detailed. We then remind briefly some of the most usual (dis-)similarity measures between histograms, paying a particular attention to a class of similarity functions, called kernels, we deeply investigate. We present several strategies to build similarity measures. These similarities can then either represent the basis of a similarity search system or be integrated into more powerful machine learning frameworks to address classification, retrieval or detection tasks.
KeywordsSupport Vector Machine Unlabeled Data Machine Learning Approach Multiple Kernel Learn Stochastic Gradient Descent
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