A comparison between various classification methods for image classification stage in CBIR

This paper's objective is to investigate and compare classification performances for different methods (Radial Basis Function networks, Support Vector Machines neural networks, Naïve Bayes and Decision Trees). The purpose of this comparison is to choose the best solution in terms of performance/computation to be included in an integrated framework for semantic image analysis that is suitable for content-based image retrieval by combining classical descriptors with classification algorithms.

[1]  Ramin Zabih,et al.  Histogram refinement for content-based image retrieval , 1996, Proceedings Third IEEE Workshop on Applications of Computer Vision. WACV'96.

[2]  Simone Santini,et al.  Exploratory Image Databases: Content-Based Retrieval , 2001 .

[3]  Radu Dogaru,et al.  A modified RBF neural network for efficient current-mode VLSI implementation , 1996, Proceedings of Fifth International Conference on Microelectronics for Neural Networks.

[4]  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).

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

[6]  Lior Rokach,et al.  Data Mining And Knowledge Discovery Handbook , 2005 .

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

[8]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[9]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[10]  Constantin Vertan,et al.  Relevance feedback approaches for MPEG-7 content-based biomedical image retrieval , 2010, 2010 8th International Conference on Communications.

[11]  Olivier Chapelle,et al.  Training a Support Vector Machine in the Primal , 2007, Neural Computation.

[12]  Radu Dogaru.  A modified Naïve Bayes classifier for efficient implementations in embedded systems , 2011, ISSCS 2011 - International Symposium on Signals, Circuits and Systems.

[13]  Radu Dogaru,et al.  An efficient finite precision RBF-M neural network architecture using support vectors , 2010, 10th Symposium on Neural Network Applications in Electrical Engineering.

[14]  Harry Zhang,et al.  The Optimality of Naive Bayes , 2004, FLAIRS.