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Sparse-Coded Features for Image Retrieval

Sparse-Coded Features for Image Retrieval

This video was recorded at British Machine Vision Conference (BMVC), Bristol 2013. State-of-the-art image retrieval systems typically represent an image with a bag of low-level features. Since different images often exhibit different kinds of low-level characteristics, it is desirable to represent an image with multiple types of complementary features. The systems scalability is, however, significantly lowered when increasing the number of feature types, as the amount of data is also increased rapidly both in index and in query representation. In this paper, we apply sparse coding to derive a compact yet discriminative image representation from multiple types of features for large-scale image retrieval. We first convert each feature descriptor into a sparse code, and aggregate each type of... Show More


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