A Novel Image Classification Algorithm Using Swarm-Based Technique for Image Database

Image data has become one of the most popular data type distributed in many multimedia applications. The effectiveness of image deployment is greatly dependent on the ability to classify and retrieve the image files based on their properties or content. However, image classification has faced a problem where the number of possible different combination of variables is very high. The algorithms which based on exhaustive search are unable to cope with the problem as the computational ability become infeasible. In this paper, a new image classification algorithm namely Simplified Swarm Optimization (SSO) has been proposed. This new approach is capable to obtain the high quality potential solution in the population which contributes to the improvement of the classification performance. This algorithm has been tested using image dataset which consists of seven classes of outdoor images. Moreover, the performance of SSO, Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) have been compared and analyzed. The testing results show that SSO is more competitive than PSO and SVM, and can be fruitfully exploited in image database and solving image classification problem.

[1]  Tieli Sun,et al.  An efficient hybrid data clustering method based on K-harmonic means and Particle Swarm Optimization , 2009, Expert Syst. Appl..

[2]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[3]  Kyoungro Yoon,et al.  Multi Class Adult Image Classification Using Neural Networks , 2005, Canadian Conference on AI.

[4]  Abdullah Al Mamun,et al.  An evolutionary memetic algorithm for rule extraction , 2010, Expert Syst. Appl..

[5]  Ming-Hseng Tseng,et al.  A genetic algorithm rule-based approach for land-cover classification , 2008 .

[6]  Chih-Fong Tsai,et al.  Image mining by spectral features: A case study of scenery image classification , 2007, Expert Syst. Appl..

[7]  Wei-Chang Yeh,et al.  A new hybrid approach for mining breast cancer pattern using discrete particle swarm optimization and statistical method , 2009, Expert Syst. Appl..

[8]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[9]  Fawaz S. Al-Anzi,et al.  A PSO and a Tabu search heuristics for the assembly scheduling problem of the two-stage distributed database application , 2006, Comput. Oper. Res..

[10]  Ziqiang Wang,et al.  Classification Rule Mining Based on Particle Swarm Optimization , 2006, RSKT.

[11]  Luca Lombardi,et al.  Image classification: an evolutionary approach , 2002, Pattern Recognit. Lett..

[12]  Ian Witten,et al.  Data Mining , 2000 .

[13]  Farid Melgani,et al.  Classification of Electrocardiogram Signals With Support Vector Machines and Particle Swarm Optimization , 2008, IEEE Transactions on Information Technology in Biomedicine.

[14]  Yaonan Wang,et al.  Texture classification using the support vector machines , 2003, Pattern Recognit..

[15]  Ebroul Izquierdo,et al.  Image classification using biologically inspired systems , 2006, MobiMedia '06.

[16]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[17]  Peng-Yeng Yin,et al.  A particle swarm optimization approach to the nonlinear resource allocation problem , 2006, Appl. Math. Comput..

[18]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[19]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .