Commenced in January 2007
Paper Count: 31108
A New Method for Image Classification Based on Multi-level Neural Networks
Abstract:In this paper, we propose a supervised method for color image classification based on a multilevel sigmoidal neural network (MSNN) model. In this method, images are classified into five categories, i.e., “Car", “Building", “Mountain", “Farm" and “Coast". This classification is performed without any segmentation processes. To verify the learning capabilities of the proposed method, we compare our MSNN model with the traditional Sigmoidal Neural Network (SNN) model. Results of comparison have shown that the MSNN model performs better than the traditional SNN model in the context of training run time and classification rate. Both color moments and multi-level wavelets decomposition technique are used to extract features from images. The proposed method has been tested on a variety of real and synthetic images.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1073503Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1364
 Goodrum, "Image Information Retrieval: An Overview of Current Research", Special Issue on Information Science Research, vol. 3, no. 2, 2000.
 N. O- Connor, E. Cooke, H. Le Borgne, M. Blighe, and T. Adamek," The aceToolbox: Lowe-Level Audiovisual Feature Extraction for Retrieval and Classification". Proc. of EWIMT-05, Nov. 2005.
 Deng, H. and D.A. Clausi,"Gaussian MRF Rotation-Invariant Features for SAR Sea Ice Classification," IEEE PAMI, 26(7): pp. 951-955, 2004.
 R. Zhao and W. I. Grosky, Bridging the Semantic Gap in Image Retrieval, Distributed Multimedia Databases: Techniques and Applications, T. K. Shih (Ed.), Idea Group Publishing, Hershey, Pennsylvania, pp. 14-36, 2001.
 J. Luo, and A. Savakis, "Indoor vs Outdoor Classification of Consumer Photographs using Low-level and Semantic Features," Proc. of ICIP, pp.745-748, 2001.
 A.K. Vailaya, Jain, and H.-J. Zhang, "On Image Classification: City Images vs. Landscapes," Pattern Recognition Journal, vol. 31, pp 1921- 1936, December, 1998.
 J. Z. Wang, G. Li, and G. Wiederhold, "SIMPLIcity: Semanticssensitive Integrated Matching for Picture LIbraries," In IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 23, pages 947-963, 2001.
 S. Prabhakar, H. Cheng, J.C. Handley, Z. Fan Y.W. Lin, "Picturegraphics Color Image Classification," Proc. of ICIP, pp. 785-788, 2002.
 Hartmann and R. Lienhart,"Automatic Classification of Images on the Web," In Proc of SPIE Storage and Retrieval for Media Databases, pp. 31-40, 2002.
 S. W. Kuffler and J. G. Nicholls, "From Neuron to Brain," (Sinauer Associates, Sunderland, 1976; Mir, Moscow, 1979).
 S. Bhattacharyya and P. Dutta, "Multiscale Object Extraction with MUSIG and MUBET with CONSENT: A Comparative Study," Proceedings of KBCS 2004, pp. 100-109, 2004.
 Yu, H., Li, M., Zhang, H.-J., Feng, J., Color texture moments for content-based image retrieval, In: Internat. Conf. on Image Processing, vol. 3, pp. 929-932, 2002.
 Der-Chiang Li and Yao-Hwei Fang, "An algorithm to cluster data for efficient classification of support vector machines," Expert Systems with Applications, vol. 34, pp. 2013-2018, 2008.
 R. Marmo et al. "Textural identification of carbonate rocks by image processing and neural network: Methodology proposal and examples," Computers and Geosciences, 31, pp. 649-659, 2005.