Compact feature based clustering for large-scale image retrieval

This paper addresses the problem of fast similar image retrieval, especially for large-scale datasets with millions of images. We present a new framework which consists of two dependent algorithms. First, a new feature is proposed to represent images, which is dubbed compact feature based clustering (CFC). For each image, we first extract cluster centers of local features, and then calculate distribution histograms of local features and statistics of spatial information in each cluster to form compact features based clustering, replacing thousands of local features. It can reduce feature vectors of image representation and enhance the discriminative power of each feature. In addition, an efficient retrieval method is proposed, based on vocabulary tree through compact features based clustering. Extensive experiments on the Ukbench, Holidays, and ImageNet databases demonstrate that our method reduces the memory and computation overhead and improves the retrieval efficiency, while keeping approximate state-of-the-art accuracy.