FAST AND COMPACT VISUAL CODEBOOK FOR LARGE-SCALE OBJECT RETRIEVAL

In this paper, we propose a novel method for learning a compact codebook in large-scale image dataset. In the past few years, bag-of-visual-words model has been proven to be effective and efficient in multiple multimedia tasks including object retrieval, object detection and scene classification. The existing codebook constructing methods, like k-means or approximate k-means, suffer from information loss in vector quantization, and limit the retrieval performance. We try to improve the existing methods in both time efficiency and retrieval accuracy. By performing principal component analysis in initialization, clustering can start with a quasi-optimal solution. A leader clustering scheme is also proposed to reduce quantization loss, which leads to a compact and discriminative codebook. Our experiment showed that, the proposed method requires less training time and yields better performance in large-scale object retrieval.