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Image Classification based on Color and Texture features using FRBFN network with Artificial Bee Colony Optimization Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 98 - Number 14 |
| Year of Publication: 2014 |
| Authors: D. Chandrakala, S. Sumathi |
10.5120/17252-7592
|
D. Chandrakala, S. Sumathi . Image Classification based on Color and Texture features using FRBFN network with Artificial Bee Colony Optimization Algorithm. International Journal of Computer Applications. 98, 14 ( July 2014), 19-29. DOI=10.5120/17252-7592
Abstract
With advances in information technology, there is an explosive growth of image databases which demands effective and efficient tools that allow users to search through this large collection. Conventionally, the way of searching the collections of digital image database is by matching keywords with image caption, descriptions and labels. Keyword based searching method provides very high computational complexity and the user has to remember the exact keywords used in the image database. Even though the computational complexities of the traditional image retrieval systems are high, they produce low classification accuracy. This paper proposes an image classification system based on combined color and texture features of an image to overcome these problems. This system consists of different stages such as image preprocessing, color and texture features extraction and fuzzy c-means radial basis function neural (FRBFN) network based classification/retrieval with Artificial Bee Colony (ABC) optimization algorithm. In this scheme, the color features are derived using Histogram Equalization method in HSV space and the texture features represented by contrast, energy, entropy, correlation and local stationary over the region in an image derived based on co-occurrence matrix. The proposed neural network based Comprehensive Image Classification (CIC) scheme fuses the low level features of the image such as color and texture to improve the systems classification performance and these features are converted as high level features by Radial Basis Function Neural Network(RBFN) with Fuzzy c-means (FCM) to fix the hidden layer neurons. The weight vectors of the network are reasonably assigned by Artificial Bee Colony (ABC) optimization algorithm. The experimental results show that the proposed method is superior to other traditional Content Based Image Retrieval (CBIR) methods in classifying the images with less computational time. The performance of proposed scheme was evaluated with a set of 600 color images taken from COREL benchmark image library and the routines of the proposed system were simulated using MATLAB R2008b.
References
- J. Yue, et. al. , "Content based image retrieval using color and texture fused features" Mathematical and Computer Modelling, 2010, Elsevier Ltd. (DOI: 10. 1016/j. mcm. 2010. 11. 044)
- R. Datta, D. Joshi, J. Li, J. Z. Wang, Image Retrieval: ideas, influences and trends of the new age, ACM Computing Surveys, 40 (2), 2008,1-60.
- P. Muneesawang, L. Guan, "An interactive approach for CBIR using a network of radial basis functions", IEEE Transactions on Multimedia, 6,2004, 703-716.
- DervisKaraboga, BahriyeAkay, "A Comparative study of Artificial Bee Colony Algorithm", Applied Mathematics and Computation, 214, 2009, 108-132.
- B. G. Prasad, K. K. Biswas, S. K. Gupta, " Region based image retrieval using integrated color, shape and location index", Computer vision and Image Understanding, vol 94, pp 193-233,2004.
- Young Deok Chun, Nam Chul Kim, Ick Hoon Jang, " Content-Based Image Retrieval using Multi-resolution color and texture features", IEEE Transaction on Multimedia, vol 10, pp. 1073-1084,2008.
- Tai X. Y, Wang L. D. , "Medical Image Retrieval Based on Color-Texture algorithm and GTI model", The second International Conference on Image Processing Bioinformatics and Biomedical Engineering, (ICBBE2008), 2008.
- Y. D. Chun, S. Y. Seo and N. C. Kim, "Image retrieval using BDIP and BVLC moments", IEEE Transaction on Circuits System Video Technology, vol 13, pp. 951-957, Sep. 2003.
- W. Niblack, R. Barber, W. Equitz, et. al. , "The QBIC project: querying images by content using color, texture and shape, SPIE 1908, San Jose, CA, 1993,pp. 173-187.
- A. Pentland, R. W. Picard, S. Scarloff, "Photbook: tools for content based manipulation of image databases, SPIE 2185, San Jose, CA, 1994, pp 34-47.
- S. Mehrotra, Y. Rui, M. Ortega, et al. "Supporting content based queries over images in MARS", Proceedings of IEEE International conference on Multimedia Computing and Systems '97, Ottawa,Ontario, Canada, 1997, pp 632-633.
- J. R. Bach, C. Fuller, A. Gupta, et al. , "Virage image search engine: an Open framework for Image Management", SPIE 2670, 23, San Jose, CA, 1996, pp. 76-87.
- J. R. Smith, "Integrated spatial and feature image systems: Retrieval, analysis and compression", Ph. D. Dissertation, Columbia University, New York City,1997.
- JZ. Wang, J. ULi, G. Wiederhold, SIMPLIcity: Semantic- Sensitive Integrated Matching for Picture Libraries", IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(9),2001, 947-963.
- Y. Rui and T. S. Huang, "Image Retrieval: Current Techniques, promising directions and open issues", Journal of Visual Communications Image Represent, vol 10, pp. 39-62, Oct 1999.
- R. M. Haralick, K. Shanmugam, I. Dinstein, "Texture features for image Classification", IEEE Transactions System Man Cybernetics, Vol. SMC-8, pp. 610-621, Nov 1973.
- S. Liapis and G. Tziritas, "Color and texture image retrieval using chromaticity histograms and wavelet frames", IEEE Transactions on Multimedia, vol 2, pp. 676-686, Oct 2004.
- H. Permuter, J. Francos and I. H. Jermyn, " Gaussian mixture models of texture and color for image database retrieval", Proceedings of IEEE International Conference Acoustics, Speech, Signal Processing, Hong Kong, April 2003, vol 3, pp. 569-572.
- Y. Gong, H. Zhang, H. Chuant and M. Skauuchi, "An image database system with contents capturing and fast image indexing abilities", Proceedings of IEEE International Conferences on Multimedia Computing and Systems, Boston, Massachusetts, USA, May 1994, pp 121-130.
- ShahroozNematipour, JamshidShanbehzadeh, Reza AskariMoghadam, "Relevance Feedback Optimization in Content Based Image Retrieval via Enhanced Radial Basis Function Network", Proceedings of the International Multi-Conference of Engineers and Computer Science (IMECS 2011) March 16-18, 2011, Hong Kong.
- Zhang, Ruofei, Zhang, Zhongfei, "BALAS: Empirical Bayesian learning in the relevance feedback for image retrieval", Image and Vision Computing, Elsevier, 2005, pp. 39-62.
- RongrongJi, Hongxun Yao, Jicheng Wang, PengfeiXu, IXianming Liu, "Clustering Based Subspace SVM ensemble for Relevance Feedback Learning", IEEE Transaction on Image processing", 2008, pp. 73-92.
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