Local Sparse Coding for Image Classification and Retrieval

The success of sparse representations in image modeling and recovery has motivated its use in computer vision applications. Object recognition has been effectively performed by aggregating sparse codes of local features in an image at multiple spatial scales. Though sparse coding guarantees a highfidelity representation, it does not exploit the dependence between the local features. By incorporating suitable locality constraints, sparse coding can be regularized to obtain similar codes for similar features. In this paper, we develop an algorithm to design dictionaries for local sparse coding of image descriptors and perform object recognition using the learned dictionaries. Furthermore, we propose to perform kernel local sparse coding in order to exploit the non-linear similarity of features and describe an algorithm to learn dictionaries when the Radial Basis Function (RBF) kernel is used. In addition, we develop a supervised local sparse coding approach for image retrieval using sub-image heterogeneous features. Simulation results for object recognition demonstrate that the two proposed algorithms achieve higher classification accuracies in comparison to other sparse coding based approaches. By performing image retrieval on the Microsoft Research CamPreprint submitted to Pattern Recognition Letters December 31, 2011 bridge image dataset, we show that incorporating supervised information into local sparse coding results in improved precision-recall rates.