Multiview Cross-Media Hashing with Semantic Consistency

Cross-media hashing is used to handle both cross-media representation and indexing simultaneously. Most existing methods attempt to bridge the semantic gap by maximizing the correlation of heterogeneous instances describing the same information object. Although these methods guarantee that such instances are close in the commonly shared space, instances describing different objects but the same category may be scattered. We propose a new cross-media hashing scheme, multiview cross-media hashing with semantic consistency (MCMHSC), to address this problem. By fully exploiting the semantic correlation and complementary information among objects, MCMHSC builds discriminative hashing codes. Experiments on two public benchmark datasets show that our proposed scheme achieves comparable or better performance compared to state-of-the-art methods in terms of accuracy and time complexity.

[1]  Zi Huang,et al.  Linear cross-modal hashing for efficient multimedia search , 2013, ACM Multimedia.

[2]  Matthijs Douze,et al.  How should we evaluate supervised hashing? , 2016, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[3]  Yao Zhao,et al.  Cross-media hashing with Centroid Approaching , 2015, 2015 IEEE International Conference on Multimedia and Expo (ICME).

[4]  Yi Zhen,et al.  A probabilistic model for multimodal hash function learning , 2012, KDD.

[5]  Michael Isard,et al.  A Multi-View Embedding Space for Modeling Internet Images, Tags, and Their Semantics , 2012, International Journal of Computer Vision.

[6]  Nikos Paragios,et al.  Data fusion through cross-modality metric learning using similarity-sensitive hashing , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Nicole Immorlica,et al.  Locality-sensitive hashing scheme based on p-stable distributions , 2004, SCG '04.

[8]  Roger Levy,et al.  A new approach to cross-modal multimedia retrieval , 2010, ACM Multimedia.

[9]  Jürgen Schmidhuber,et al.  Multimodal Similarity-Preserving Hashing , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Zi Huang,et al.  Multiple feature hashing for real-time large scale near-duplicate video retrieval , 2011, ACM Multimedia.

[11]  Qi Tian,et al.  SIFT Meets CNN: A Decade Survey of Instance Retrieval , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Wenwu Zhu,et al.  Deep Multimodal Hashing with Orthogonal Regularization , 2015, IJCAI.

[13]  Dongqing Zhang,et al.  Large-Scale Supervised Multimodal Hashing with Semantic Correlation Maximization , 2014, AAAI.

[14]  Raghavendra Udupa,et al.  Learning Hash Functions for Cross-View Similarity Search , 2011, IJCAI.

[15]  Jianmin Wang,et al.  Semantics-preserving hashing for cross-view retrieval , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Guiguang Ding,et al.  Latent semantic sparse hashing for cross-modal similarity search , 2014, SIGIR.

[17]  Guiguang Ding,et al.  Collective Matrix Factorization Hashing for Multimodal Data , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.