Efficiently Retrieving Images that We Perceived as Similar

Despite growing interest in using sparse coding based methods for image classification and retrieval, progress in this direction has been limited by the high computational cost for generating each image's sparse representation. To overcome this problem, we leverage sparsity-based dictionary learning and hash-based feature selection to build a novel unsupervised way to efficiently pick out a query image's most important high-level features; the selected set of features effectively pinpoint to which group of images we would visually perceived the query as similar. Moreover, the method is adaptive to the retrieval database presented at the moment. The preliminary results based on L1 feature map show the method's efficiency and accuracy from the visual cognitive perspective.