Mobile product search with bag of hash bits
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Proceedings of the 21st international conference on World Wide Web
Manhattan hashing for large-scale image retrieval
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Boosting multi-kernel locality-sensitive hashing for scalable image retrieval
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Sequential spectral learning to hash with multiple representations
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Sparse hashing for fast multimedia search
ACM Transactions on Information Systems (TOIS)
Image search—from thousands to billions in 20 years
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special Sections on the 20th Anniversary of ACM International Conference on Multimedia, Best Papers of ACM Multimedia 2012
Order preserving hashing for approximate nearest neighbor search
Proceedings of the 21st ACM international conference on Multimedia
Hash Bit Selection Using Markov Process for Approximate Nearest Neighbor Search
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Efficient binary code indexing with pivot based locality sensitive clustering
Multimedia Tools and Applications
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Similarity search, namely, finding approximate nearest neighborhoods, is the core of many large scale machine learning or vision applications. Recently, many research results demonstrate that hashing with compact codes can achieve promising performance for large scale similarity search. However, most of the previous hashing methods with compact codes only model and optimize the search accuracy. Search time, which is an important factor for hashing in practice, is usually not addressed explicitly. In this paper, we develop a new scalable hashing algorithm with joint optimization of search accuracy and search time simultaneously. Our method generates compact hash codes for data of general formats with any similarity function. We evaluate our method using diverse data sets up to 1 million samples (e.g., web images). Our comprehensive results show the proposed method significantly outperforms several state-of-the-art hashing approaches.