Image Annotation Based on Bi-Coded Chromosome Genetic Algorithm for Feature Selection

摘要：

In image annotation system,performance is highly desired.We use a bi-coded chromosome-based genetic algorithm to optimize the weights of Multimedia Content Description Interface (MPEG-7)feature descriptors and select optimal feature descriptor subset simultaneously.Two genetic codes are used:a real code represents the weights corresponding to MPEG-7 descriptors;a binary one denotes the presence or absence of feature descriptors in the optimal descriptor subset.The genetic algorithm fitness function takes into account support vector machine s classification accuracy and the number of selected feature descriptors.The result of experiments over 2000 classified Corel images shows that the approach selects 4 of 25 MPEG-7 feature descriptors as optimal feature descriptor subset as well as corresponding optimized weights.With the selected optimal feature descriptor subset and weights,the accuracy and efficiency of image annotation system can be improved.

关键词： Feature selection image annotation genetic algorithm support vector machine multimedia content description interface.

作者: Jianjiang Lu Qi Xiao Tianzhong Zhao Yafei Zhang Yanhui Li

作者单位: Institute of Command Automation,PLA University of Science and Technology,Nanjing 210007,China Institute of Command Automation,PLA University of Science and Technology,Nanjing 210007,China;Zhenji School of Computer Science and Engineering,Southeast University,Nanjing 210096,China

会议类型: 国际会议

会议名称: 2008 IEEE International Conference on Onformation and Automation(IEEE 信息与自动化国际会议)

会议地点: 张家界

会议语种:英文

页码: 708-712

在线出版日期: 2008-06-20（万方平台首次上网日期，不代表论文的发表时间）