Hash Learning with Conditional Random Field and Rank Preserving Boosting

• Main Authors: Chun-Che Wu, 吳君哲
• Other Authors: Winston H. Hsu
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/12304719885042236924

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 102 === Transforming data into binary codes for Approximate Nearest Neighbor (ANN) search has caught lots of attention in recent years. Two major advantages of binary codes are dramatically reducing the search time and storage. To make the codes more discriminative and more compact, it is crucial to leverage the (partial) pair-wise label1/similarity information if provided. However, binary code learning is an integer programming problem, which is NP-hard. So, one promising branch of solutions is to relax the problem into real value domain as an eigen-problem. Nevertheless, the relaxation will introduce additional errors which will bias the learning results due to the quadratic objective function. Hence, we treat the hash generation process in a novel aspect, i.e., (binary) labeling problem with prior knowledge. That is, we propose a binary code enhancement method to suppress the bias effect resulting from eigen-based solutions, and model the problem into Conditional Random Field (CRF). Moreover, we also adopt the boosting scheme to preserve the distance (rank) in Hamming space. Our experimental results show that our framework has significant improvement compared to the prior works.