Supervised Multi-scale Locality Sensitive Hashing

LSH is a popular framework to generate compact representations of multimedia data, which can be used for content based search. However, the performance of LSH is limited by its unsupervised nature and the underlying feature scale. In this work, we propose to improve LSH by incorporating two elements - supervised hash bit selection and multi-scale feature representation. First, a feature vector is represented by multiple scales. At each scale, the feature vector is divided into segments. The size of a segment is decreased gradually to make the representation correspond to a coarse-to-fine view of the feature. Then each segment is hashed to generate more bits than the target hash length. Finally the best ones are selected from the hash bit pool according to the notion of bit reliability, which is estimated by bit-level hypothesis testing. Extensive experiments have been performed to validate the proposal in two applications: near-duplicate image detection and approximate feature distance estimation. We first demonstrate that the feature scale can influence performance, which is often a neglected factor. Then we show that the proposed supervision method is effective. In particular, the performance increases with the size of the hash bit pool. Finally, the two elements are put together. The integrated scheme exhibits further improved performance.EICPCI-S(ISTP)259-26