PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Semantic hashing using tags and topic modeling
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Comparing apples to oranges: a scalable solution with heterogeneous hashing
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Topology preserving hashing for similarity search
Proceedings of the 21st ACM international conference on Multimedia
Order preserving hashing for approximate nearest neighbor search
Proceedings of the 21st ACM international conference on Multimedia
Linear cross-modal hashing for efficient multimedia search
Proceedings of the 21st ACM international conference on Multimedia
Query-dependent visual dictionary adaptation for image reranking
Proceedings of the 21st ACM international conference on Multimedia
Improved binary feature matching through fusion of hamming distance and fragile bit weight
Proceedings of the 3rd ACM international workshop on Interactive multimedia on mobile & portable devices
A unified approximate nearest neighbor search scheme by combining data structure and hashing
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Smart hashing update for fast response
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Parametric local multimodal hashing for cross-view similarity search
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Mixed image-keyword query adaptive hashing over multilabel images
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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Recent years have witnessed the growing popularity of hashing in large-scale vision problems. It has been shown that the hashing quality could be boosted by leveraging supervised information into hash function learning. However, the existing supervised methods either lack adequate performance or often incur cumbersome model training. In this paper, we propose a novel kernel-based supervised hashing model which requires a limited amount of supervised information, i.e., similar and dissimilar data pairs, and a feasible training cost in achieving high quality hashing. The idea is to map the data to compact binary codes whose Hamming distances are minimized on similar pairs and simultaneously maximized on dissimilar pairs. Our approach is distinct from prior works by utilizing the equivalence between optimizing the code inner products and the Hamming distances. This enables us to sequentially and efficiently train the hash functions one bit at a time, yielding very short yet discriminative codes. We carry out extensive experiments on two image benchmarks with up to one million samples, demonstrating that our approach significantly outperforms the state-of-the-arts in searching both metric distance neighbors and semantically similar neighbors, with accuracy gains ranging from 13% to 46%.