Robust image retrieval with hidden classes

HighlightsWe address a new robust problem, named hidden classes, in content-based image retrieval (CBIR).We propose a new robust CBIR scheme using multi-image queries to tackle the difficule problem.We develop a novel query detection technique to separate queries as common or novel according to their underlying classes.We apply a self-adaptive retrieval strategy to handle different types of queries automatically.We design and carry out a number of experiments to demonstrate the effectiveness of our scheme. For the purpose of content-based image retrieval (CBIR), image classification is important to help improve the retrieval accuracy and speed of the retrieval process. However, the CBIR systems that employ image classification suffer from the problem of hidden classes. The queries associated with hidden classes cannot be accurately answered using a traditional CBIR system. To address this problem, a robust CBIR scheme is proposed that incorporates a novel query detection technique and a self-adaptive retrieval strategy. A number of experiments carried out on the two popular image datasets demonstrate the effectiveness of the proposed scheme.

[1]  Malik Yousef,et al.  One-Class SVMs for Document Classification , 2002, J. Mach. Learn. Res..

[2]  Marcel Worring,et al.  Adding Semantics to Detectors for Video Retrieval , 2007, IEEE Transactions on Multimedia.

[3]  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.

[4]  Meng Wang,et al.  Correlative Linear Neighborhood Propagation for Video Annotation , 2009, IEEE Trans. Syst. Man Cybern. Part B.

[5]  John Platt,et al.  Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .

[6]  Andrew R. Webb,et al.  Statistical Pattern Recognition , 1999 .

[7]  Thomas Sikora,et al.  The MPEG-7 visual standard for content description-an overview , 2001, IEEE Trans. Circuits Syst. Video Technol..

[8]  Ludmila I. Kuncheva,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2004 .

[9]  Keqiu Li,et al.  An effective solution for trademark image retrieval by combining shape description and feature matching , 2010, Pattern Recognit..

[10]  Manik Varma,et al.  Learning The Discriminative Power-Invariance Trade-Off , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[11]  B. S. Manjunath,et al.  Color and texture descriptors , 2001, IEEE Trans. Circuits Syst. Video Technol..

[12]  Anil K. Jain,et al.  Image classification for content-based indexing , 2001, IEEE Trans. Image Process..

[13]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[14]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

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

[16]  Jitendra Malik,et al.  SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[17]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[18]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[19]  Robert Marti,et al.  Which is the best way to organize/classify images by content? , 2007, Image Vis. Comput..

[20]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Christoph H. Lampert,et al.  Beyond sliding windows: Object localization by efficient subwindow search , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Thomas S. Huang,et al.  One-class SVM for learning in image retrieval , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[23]  Subhash C. Bagui,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.

[24]  Sameer Singh,et al.  An approach to novelty detection applied to the classification of image regions , 2004, IEEE Transactions on Knowledge and Data Engineering.

[25]  Xiaoou Tang,et al.  Real time google and live image search re-ranking , 2008, ACM Multimedia.

[26]  Shuicheng Yan,et al.  Inferring semantic concepts from community-contributed images and noisy tags , 2009, ACM Multimedia.

[27]  James Ze Wang,et al.  Image retrieval: Ideas, influences, and trends of the new age , 2008, CSUR.

[28]  Rajat Raina,et al.  Self-taught learning: transfer learning from unlabeled data , 2007, ICML '07.

[29]  Sameer Singh,et al.  Novelty detection: a review - part 2: : neural network based approaches , 2003, Signal Process..

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

[31]  Jake K. Aggarwal,et al.  Feature Integration, Multi-image Queries and Relevance Feedback in Image Retrieval , 2003 .

[32]  Eli Shechtman,et al.  In defense of Nearest-Neighbor based image classification , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[33]  Edward Y. Chang,et al.  Why one example is not enough for an image query , 2004, 2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763).

[34]  James Ze Wang,et al.  SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[35]  Nuno Vasconcelos,et al.  Bridging the Gap: Query by Semantic Example , 2007, IEEE Transactions on Multimedia.

[36]  Jun Zhang,et al.  Content Based Image Retrieval Using Unclean Positive Examples , 2009, IEEE Transactions on Image Processing.

[37]  Tat-Seng Chua,et al.  NUS-WIDE: a real-world web image database from National University of Singapore , 2009, CIVR '09.