Sparse transfer learning for interactive video search reranking

Visual reranking is effective to improve the performance of the text-based video search. However, existing reranking algorithms can only achieve limited improvement because of the well-known semantic gap between low-level visual features and high-level semantic concepts. In this article, we adopt interactive video search reranking to bridge the semantic gap by introducing user's labeling effort. We propose a novel dimension reduction tool, termed sparse transfer learning (STL), to effectively and efficiently encode user's labeling information. STL is particularly designed for interactive video search reranking. Technically, it (a) considers the pair-wise discriminative information to maximally separate labeled query relevant samples from labeled query irrelevant ones, (b) achieves a sparse representation for the subspace to encodes user's intention by applying the elastic net penalty, and (c) propagates user's labeling information from labeled samples to unlabeled samples by using the data distribution knowledge. We conducted extensive experiments on the TRECVID 2005, 2006 and 2007 benchmark datasets and compared STL with popular dimension reduction algorithms. We report superior performance by using the proposed STL-based interactive video search reranking.

[1]  Wei-Ying Ma,et al.  Benchmarking of image features for content-based retrieval , 1998, Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284).

[2]  Zhigang Luo,et al.  Manifold Regularized Discriminative Nonnegative Matrix Factorization With Fast Gradient Descent , 2011, IEEE Transactions on Image Processing.

[3]  Dacheng Tao,et al.  Biased Discriminant Euclidean Embedding for Content-Based Image Retrieval , 2010, IEEE Transactions on Image Processing.

[4]  Meng Wang,et al.  Correlative multilabel video annotation with temporal kernels , 2008, TOMCCAP.

[5]  K. Sparck Jones,et al.  Simple, proven approaches to text retrieval , 1994 .

[6]  R. Tibshirani,et al.  Least angle regression , 2004, math/0406456.

[7]  Dacheng Tao,et al.  Max-Min Distance Analysis by Using Sequential SDP Relaxation for Dimension Reduction , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Rong Yan,et al.  Multimedia Search with Pseudo-relevance Feedback , 2003, CIVR.

[9]  Thomas S. Huang,et al.  Small sample learning during multimedia retrieval using BiasMap , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[10]  Sang Uk Lee,et al.  Efficient video indexing scheme for content-based retrieval , 1999, IEEE Trans. Circuits Syst. Video Technol..

[11]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Marcel Worring,et al.  Optimization of interactive visual-similarity-based search , 2008, TOMCCAP.

[13]  Rong Yan,et al.  Semantic concept-based query expansion and re-ranking for multimedia retrieval , 2007, ACM Multimedia.

[14]  Shih-Fu Chang,et al.  Video search reranking via information bottleneck principle , 2006, MM '06.

[15]  Xindong Wu,et al.  Manifold elastic net: a unified framework for sparse dimension reduction , 2010, Data Mining and Knowledge Discovery.

[16]  Xian-Sheng Hua,et al.  Video search re-ranking via multi-graph propagation , 2007, ACM Multimedia.

[17]  Hwann-Tzong Chen,et al.  Semantic manifold learning for image retrieval , 2005, ACM Multimedia.

[18]  Dacheng Tao,et al.  Bregman Divergence-Based Regularization for Transfer Subspace Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[19]  Xuelong Li,et al.  A unifying framework for spectral analysis based dimensionality reduction , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[20]  Xiaofei He,et al.  Locality Preserving Projections , 2003, NIPS.

[21]  Rong Yan,et al.  Co-retrieval: A Boosted Reranking Approach for Video Retrieval , 2004, CIVR.

[22]  Xuelong Li,et al.  Direct kernel biased discriminant analysis: a new content-based image retrieval relevance feedback algorithm , 2006, IEEE Transactions on Multimedia.

[23]  Shih-Fu Chang,et al.  Video search reranking through random walk over document-level context graph , 2007, ACM Multimedia.

[24]  Xian-Sheng Hua,et al.  Active Reranking for Web Image Search , 2010, IEEE Transactions on Image Processing.

[25]  C.-C. Jay Kuo,et al.  Texture analysis and classification with tree-structured wavelet transform , 1993, IEEE Trans. Image Process..

[26]  Dacheng Tao,et al.  Backward-Forward Least Angle Shrinkage for Sparse Quadratic Optimization , 2010, ICONIP.

[27]  Yuxiao Hu,et al.  Nonlinear Discriminant Analysis on Embedded Manifold , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

[28]  Jiawei Han,et al.  Semi-supervised Discriminant Analysis , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[29]  Arnold Neumaier,et al.  Solving Ill-Conditioned and Singular Linear Systems: A Tutorial on Regularization , 1998, SIAM Rev..

[30]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[31]  Xuelong Li,et al.  Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  张振跃,et al.  Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment , 2004 .

[33]  Lifeng Sun,et al.  Topic mining on web-shared videos , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[34]  I. Andreadis,et al.  Colour histogram content-based image retrieval and hardware implementation , 2003 .

[35]  Xiaofei He,et al.  Using Graph Model for Face Analysis , 2005 .

[36]  Thomas S. Huang,et al.  Content-based image retrieval with relevance feedback in MARS , 1997, Proceedings of International Conference on Image Processing.

[37]  J. J. Rocchio,et al.  Relevance feedback in information retrieval , 1971 .

[38]  Bo Yang,et al.  DSI: A model for distributed multimedia semantic indexing and content integration , 2010, TOMCCAP.

[39]  H. Hotelling.  Analysis of a complex of statistical variables into principal components. , 1933 .

[40]  Xian-Sheng Hua,et al.  Bayesian video search reranking , 2008, ACM Multimedia.

[41]  Nicu Sebe,et al.  Content-based multimedia information retrieval: State of the art and challenges , 2006, TOMCCAP.

[42]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[43]  Naftali Tishby,et al.  Agglomerative Information Bottleneck , 1999, NIPS.

[44]  Gerard Salton,et al.  The SMART Retrieval System—Experiments in Automatic Document Processing , 1971 .

[45]  Gustavo Carneiro,et al.  Supervised Learning of Semantic Classes for Image Annotation and Retrieval , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[46]  R. Manmatha,et al.  Automatic image annotation and retrieval using cross-media relevance models , 2003, SIGIR.

[47]  Newton Lee,et al.  ACM Transactions on Multimedia Computing, Communications and Applications (ACM TOMCCAP) , 2007, CIE.

[48]  Jiawei Han,et al.  Spectral Regression: A Unified Approach for Sparse Subspace Learning , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[49]  Jing Huang,et al.  Image indexing using color correlograms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[50]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[51]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[52]  Shumeet Baluja,et al.  VisualRank: Applying PageRank to Large-Scale Image Search , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[53]  Thomas S. Huang,et al.  Relevance feedback: a power tool for interactive content-based image retrieval , 1998, IEEE Trans. Circuits Syst. Video Technol..