A Novel Method of Mapping Semantic Gap to Classify Natural Images

摘要：

There exists an enormous gap between low-level visual feature and high-level semantic information, and the accuracy of content-based image classification and retrieval depends greatly on the description of low-level visual features. A novel method of mapping semantic gap is presented in this paper, which first extracts color and texture features from images after adaptive thresholding segmentation. Then, we use the BP neural network to map low-features to high-level semantic features. Experimental results show the efficacy of the proposed system.

关键词： semantic gap content-based image retrieval imageclassification color tezture adaptive thresholding segmentation BP neural network

作者: Xiao Ping Shi Yuexiang Xie Wenlan

作者单位: School of Information Engineering, Xingtan University Xiangtan 411105, China

会议类型: 国际会议

会议名称: 2009图像分析与信号处理国际会议(2009 International Conference on Image Analysis and Signal Processing)

会议地点: 浙江台州

会议语种:英文

页码: 166-171

在线出版日期: 2009-04-11（万方平台首次上网日期，不代表论文的发表时间）