Adaptive tree similarity learning for image retrieval

Abstract.Learning-enhanced relevance feedback is one of the most promising and active research directions in content-based image retrieval in recent years. However, the existing approaches either require prior knowledge of the data or converge slowly and are thus not coneffective. Motivated by the successful history of optimal adaptive filters, we present a new approach to interactive image retrieval based on an adaptive tree similarity model to solve these difficulties. The proposed tree model is a hierarchical nonlinear Boolean representation of a user query concept. Each path of the tree is a clustering pattern of the feedback samples, which is small enough and local in the feature space that it can be approximated by a linear model nicely. Because of the linearity, the parameters of the similartiy model are better learned by the optimal adaptive filter, which does not require any prior knowledge of the data and supports incremental learning with a fast convergence rate. The proposed approach is simple to implement and achieves better performance than most approaches. To illustrate the performance of the proposed approach, extensive experiments have been carried out on a large heterogeneous image collection with 17,000 images, which render promising results on a wide variety of queries.

[1]  Jing Peng.  Adaptive Multi-class Metric Content-Based Image Retrieval , 2000, VISUAL.

[2]  Tai-Kuo Woo.  HRLS: a more efficient RLS algorithm for adaptive FIR filtering , 2001, IEEE Commun. Lett..

[3]  Ishwar K. Sethi,et al.  Media content management , 2001 .

[4]  Christos Faloutsos,et al.  MindReader: Querying Databases Through Multiple Examples , 1998, VLDB.

[5]  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).

[6]  Qiang Yang,et al.  A unified framework for semantics and feature based relevance feedback in image retrieval systems , 2000, ACM Multimedia.

[7]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[8]  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).

[9]  Tao Wang,et al.  A hierarchical characterization scheme for image retrieval , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[10]  C. Krumhansl.  Concerning the Applicability of Geometric Models to Similarity Data : The Interrelationship Between Similarity and Spatial Density , 2005 .

[11]  Peter Stanchev,et al.  Content-Based Image Retrieval Systems , 2001 .

[12]  Kerry Rodden,et al.  Does organisation by similarity assist image browsing? , 2001, CHI.

[13]  Gerard V. Trunk,et al.  A Problem of Dimensionality: A Simple Example , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Bernard Widrow,et al.  Adaptive switching circuits , 1988 .

[15]  James Ze Wang,et al.  IRM: integrated region matching for image retrieval , 2000, ACM Multimedia.

[16]  Ingemar J. Cox,et al.  The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments , 2000, IEEE Trans. Image Process..

[17]  Shi-Min Hu,et al.  Optimal adaptive learning for image retrieval , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[18]  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).

[19]  Tom Minka,et al.  Interactive learning with a "society of models" , 1997, Pattern Recognit..

[20]  Ramesh C. Jain,et al.  Similarity measures for image databases , 1995, Electronic Imaging.

[21]  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).

[22]  Rosalind W. Picard,et al.  Interactive Learning Using a "Society of Models" , 2017, CVPR 1996.

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

[24]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[25]  Ingemar J. Cox,et al.  PicHunter: Bayesian relevance feedback for image retrieval , 1996, Proceedings of 13th International Conference on Pattern Recognition.

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

[27]  Gary Marchionini,et al.  Digital libraries: Introduction , 2001, CACM.

[28]  Qi Tian,et al.  Update relevant image weights for content-based image retrieval using support vector machines , 2000, 2000 IEEE International Conference on Multimedia and Expo. ICME2000. Proceedings. Latest Advances in the Fast Changing World of Multimedia (Cat. No.00TH8532).

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

[30]  Thomas S. Huang,et al.  Supporting Ranked Boolean Similarity Queries in MARS , 1998, IEEE Trans. Knowl. Data Eng..

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

[32]  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.

[33]  Suk I. Yoo,et al.  A Neural Network-Based Image Retrieval Using Nonlinear Combination of Heterogeneous Features , 2001, Int. J. Comput. Intell. Appl..

[34]  Simon Haykin,et al.  Adaptive filter theory (2nd ed.) , 1991 .

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

[36]  Ben Bradshaw,et al.  Semantic based image retrieval: a probabilistic approach , 2000, ACM Multimedia.

[37]  Vijay V. Raghavan,et al.  Content-Based Image Retrieval Systems - Guest Editors' Introduction , 1995, Computer.

[38]  Chi-Ren Shyu,et al.  Relevance feedback decision trees in content-based image retrieval , 2000, 2000 Proceedings Workshop on Content-based Access of Image and Video Libraries.

[39]  Juyang Weng,et al.  Using Discriminant Eigenfeatures for Image Retrieval , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[40]  Thomas S. Huang,et al.  Comparing discriminating transformations and SVM for learning during multimedia retrieval , 2001, MULTIMEDIA '01.

[41]  Wei-Ying Ma,et al.  Information embedding based on user's relevance feedback for image retrieval , 1999, Optics East.