Active Learning for Relevance Feedback in Image Retrieval

AbstrAct Relevance.In.Co-SVM algorithm, color and texture are naturally considered as sufficient and.uncorrelated.views.of.an.image..SVM classifier is learned in color and texture feature subspaces, respectively. Then the two classifiers are used to classify the unlabeled data. These unlabeled samples that disagree in the two classifiers are chose to label. The extensive experiments show that the proposed algorithm is beneficial to image retrieval.

[1]  Lei Wang,et al.  Bootstrapping SVM active learning by incorporating unlabelled images for image retrieval , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[2]  Rayid Ghani,et al.  Analyzing the effectiveness and applicability of co-training , 2000, CIKM '00.

[3]  Greg Schohn,et al.  Less is More: Active Learning with Support Vector Machines , 2000, ICML.

[4]  Tong Zhang,et al.  The Value of Unlabeled Data for Classification Problems , 2000, ICML 2000.

[5]  Hanqing Lu,et al.  Weighted Co-SVM for Image Retrieval with MVB Strategy , 2007, 2007 IEEE International Conference on Image Processing.

[6]  Yoram Singer,et al.  Unsupervised Models for Named Entity Classification , 1999, EMNLP.

[7]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[8]  Shih-Fu Chang,et al.  Image Retrieval: Current Techniques, Promising Directions, and Open Issues , 1999, J. Vis. Commun. Image Represent..

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

[10]  Tom M. Mitchell,et al.  Generalization as Search , 2002 .

[11]  Hanqing Lu,et al.  Selective Sampling Based on Dynamic Certainty Propagation for Image Retrieval , 2008, MMM.

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

[13]  Kongqiao Wang,et al.  Active learning for image retrieval with Co-SVM , 2007, Pattern Recognit..

[14]  Daphne Koller,et al.  Support Vector Machine Active Learning with Applications to Text Classification , 2000, J. Mach. Learn. Res..

[15]  Craig A. Knoblock,et al.  Selective Sampling with Redundant Views , 2000, AAAI/IAAI.

[16]  H. Sebastian Seung,et al.  Selective Sampling Using the Query by Committee Algorithm , 1997, Machine Learning.

[17]  Qi Tian,et al.  Incorporate support vector machines to content-based image retrieval with relevance feedback , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[18]  Sanjoy Dasgupta,et al.  PAC Generalization Bounds for Co-training , 2001, NIPS.

[19]  Avrim Blum,et al.  The Bottleneck , 2021, Monopsony Capitalism.

[20]  James Ze Wang,et al.  Content-based image retrieval: approaches and trends of the new age , 2005, MIR '05.

[21]  Paul A. Viola,et al.  Boosting Image Retrieval , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[22]  Thomas S. Huang,et al.  Relevance feedback in image retrieval: A comprehensive review , 2003, Multimedia Systems.

[23]  Thorsten Joachims,et al.  Transductive Inference for Text Classification using Support Vector Machines , 1999, ICML.

[24]  Sriram Mohan,et al.  Conceptual Modeling for XML: A Myth or a Reality , 2009 .

[25]  Thomas S. Huang,et al.  Image retrieval with relevance feedback: from heuristic weight adjustment to optimal learning methods , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

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

[27]  Zhi-Hua Zhou,et al.  Semi-Supervised Regression with Co-Training , 2005, IJCAI.

[28]  Ion Muslea,et al.  Active Learning with Multiple Views , 2009, Encyclopedia of Data Warehousing and Mining.

[29]  Dana Angluin,et al.  Queries and concept learning , 1988, Machine Learning.

[30]  Edward Y. Chang,et al.  Support vector machine active learning for image retrieval , 2001, MULTIMEDIA '01.

[31]  A. Lippman,et al.  Bayesian relevance feedback for content-based image retrieval , 2000, 2000 Proceedings Workshop on Content-based Access of Image and Video Libraries.

[32]  Tom Minka,et al.  Modeling user subjectivity in image libraries , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.