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|Title:||Optimizing ranking measures for compact binary code learning|
|Citation:||Computer Vision - ECCV 2014, 13th European Conference, Zurich, Switzerland, September 6-12, 2014: Proceedings, Part III, 2014 / Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (ed./s), vol.8691 LNCS, iss.PART 3, pp.613-627|
|Publisher:||Springer International Publishing|
|Series/Report no.:||Lecture Notes in Computer Science; 8691|
|Conference Name:||13th European Conference on Computer Vision (ECCV 2014) (06 Sep 2014 - 12 Sep 2014 : Zurich, Switzerland)|
|Guosheng Lin, Chunhua Shen, Jianxin Wu|
|Abstract:||Hashing has proven a valuable tool for large-scale information retrieval. Despite much success, existing hashing methods optimize over simple objectives such as the reconstruction error or graph Laplacian related loss functions, instead of the performance evaluation criteria of interest-multivariate performance measures such as the AUC and NDCG. Here we present a general framework (termed StructHash) that allows one to directly optimize multivariate performance measures. The resulting optimization problem can involve exponentially or infinitely many variables and constraints, which is more challenging than standard structured output learning. To solve the StructHash optimization problem, we use a combination of column generation and cutting-plane techniques. We demonstrate the generality of StructHash by applying it to ranking prediction and image retrieval, and show that it outperforms a few state-of-the-art hashing methods.|
|Rights:||© Springer International Publishing Switzerland 2014|
|Appears in Collections:||Computer Science publications|
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