Boosting Image Retrieval

We present an approach for image retrieval using a very large number of highly selective features and efficient learning of queries. Our approach is predicated on the assumption that each image is generated by a sparse set of visual “causes” and that images which are visually similar share causes. We propose a mechanism for computing a very large number of highly selective features which capture some aspects of this causal structure (in our implementation there are over 46,000 highly selective features). At query time a user selects a few example images, and the AdaBoost algorithm is used to learn a classification function which depends on a small number of the most appropriate features. This yields a highly efficient classification function. In addition we show that the AdaBoost framework provides a natural mechanism for the incorporation of relevance feedback. Finally we show results on a wide variety of image queries.

[1]  Edward H. Adelson,et al.  The Laplacian Pyramid as a Compact Image Code , 1983, IEEE Trans. Commun..

[2]  D. Freedman,et al.  Asymptotics of Graphical Projection Pursuit , 1984 .

[3]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.

[4]  Don R. Hush,et al.  Query by image example: The CANDID approach , 1995 .

[5]  David Salesin,et al.  Fast multiresolution image querying , 1995, SIGGRAPH.

[6]  Michael Stonebraker,et al.  Chabot: Retrieval from a Relational Database of Images , 1995, Computer.

[7]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[8]  Dragutin Petkovic,et al.  Query by Image and Video Content: The QBIC System , 1995, Computer.

[9]  Edoardo Ardizzone,et al.  JACOB: just a content-based query system for video databases , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[10]  Edward H. Adelson,et al.  Noise removal via Bayesian wavelet coring , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[11]  Amarnath Gupta,et al.  Virage image search engine: an open framework for image management , 1996, Electronic Imaging.

[12]  Paul A. Viola,et al.  Structure Driven Image Database Retrieval , 1997, NIPS.

[13]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[14]  Yoav Freund,et al.  Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.

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

[16]  Clement T. Yu,et al.  A system for effective content based image retrieval , 1997, MULTIMEDIA '96.

[17]  Mohamed Abdel-Mottaleb,et al.  CONIVAS: content-based image and video access system , 1997, MULTIMEDIA '96.

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

[19]  W. Eric L. Grimson,et al.  Configuration based scene classification and image indexing , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[20]  Yali Amit,et al.  Shape Quantization and Recognition with Randomized Trees , 1997, Neural Computation.

[21]  Shih-Fu Chang,et al.  VisualSEEk: a fully automated content-based image query system , 1997, MULTIMEDIA '96.

[22]  Takeo Kanade,et al.  Neural Network-Based Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Chahab Nastar,et al.  Efficient query refinement for image retrieval , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[24]  Oded Maron,et al.  Multiple-Instance Learning for Natural Scene Classification , 1998, ICML.

[25]  Tomaso A. Poggio,et al.  A general framework for object detection , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[26]  Ingemar J. Cox,et al.  An optimized interaction strategy for Bayesian relevance feedback , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[27]  Jitendra Malik,et al.  Color- and texture-based image segmentation using EM and its application to content-based image retrieval , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

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

[29]  Qi Tian,et al.  Discriminant-EM algorithm with application to image retrieval , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

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

[31]  Nuno Vasconcelos,et al.  A probabilistic architecture for content-based image retrieval , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[32]  Thomas S. Huang,et al.  Optimizing learning in image retrieval , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[33]  Paul A. Viola,et al.  Robust Real-time Object Detection , 2001 .

[34]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[35]  Wei-Ying Ma,et al.  Learning similarity measure for natural image retrieval with relevance feedback , 2002, IEEE Trans. Neural Networks.

[36]  Alex Pentland,et al.  Photobook: Content-based manipulation of image databases , 1996, International Journal of Computer Vision.

[37]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[38]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[39]  David A. Cohn,et al.  Improving generalization with active learning , 1994, Machine Learning.