A comparison of active classification methods for content-based image retrieval

This paper deals with content-based image indexing and category retrieval in general databases. Statistical learning approaches have been recently introduced in CBIR. Labelled images are considered as training data in learning strategy based on classification process. We introduce an active learning strategy to select the most difficult images to classify with only few training data. Experimentations are carried out on the COREL database. We compare seven classification strategies to evaluate the active learning contribution in CBIR.

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

[2]  Colin Campbell,et al.  Algorithmic approaches to training Support Vector Machines: a survey , 2000, ESANN.

[3]  Ben A. M. Schouten,et al.  Show Me What You Mean! Pariss: A CBIR-Interface that Learns by Example , 2000, VISUAL.

[4]  Patrick Haffner,et al.  Support vector machines for histogram-based image classification , 1999, IEEE Trans. Neural Networks.

[5]  Cordelia Schmid,et al.  Weakly Supervised Learning of Visual Models and Its Application to Content-Based Retrieval , 2004, International Journal of Computer Vision.

[6]  Trevor Hastie,et al.  The elements of statistical learning. 2001 , 2001 .

[7]  Edward Y. Chang,et al.  Statistical learning for effective visual information retrieval , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

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

[9]  Aleksandra Mojsilovic,et al.  Capturing image semantics with low-level descriptors , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

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

[11]  Luc Van Gool,et al.  Content-Based Image Retrieval Based on Local Affinely Invariant Regions , 1999, VISUAL.

[12]  Rosalind W. Picard.  A Society of Models for Video and Image Libraries , 1996, IBM Syst. J..

[13]  John P. Eakins,et al.  Towards intelligent image retrieval , 2002, Pattern Recognit..

[14]  Patrick Gros,et al.  Content-based Retrieval Using Local Descriptors: Problems and Issues from a Database Perspective , 2001, Pattern Analysis & Applications.

[15]  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).

[16]  David J. Spiegelhalter,et al.  Machine Learning, Neural and Statistical Classification , 2009 .

[17]  Matthieu Cord,et al.  RETIN: A Content-Based Image Indexing and Retrieval System , 2001, Pattern Analysis & Applications.

[18]  Thorsten Joachims,et al.  A statistical learning learning model of text classification for support vector machines , 2001, SIGIR '01.

[19]  Simone Santini,et al.  Emergent Semantics through Interaction in Image Databases , 2001, IEEE Trans. Knowl. Data Eng..

[20]  Vapnik,et al.  SVMs for Histogram Based Image Classification , 1999 .

[21]  Giuseppe Patanè,et al.  The enhanced LBG algorithm , 2001, Neural Networks.

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

[23]  J. Lafferty,et al.  Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.

[24]  Le Saux,et al.  Classification non exclusive et personnalisation par apprentissage : application à la navigation dans les bases d'images , 2003 .

[25]  Nello Cristianini,et al.  Controlling the Sensitivity of Support Vector Machines , 1999 .

[26]  David A. Cohn,et al.  Active Learning with Statistical Models , 1996, NIPS.

[27]  Nuno Vasconcelos,et al.  Bayesian models for visual information retrieval , 2000 .

[28]  Murat Kunt,et al.  Content-based retrieval from image databases: current solutions and future directions , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).