SSVR-BASED IMAGE SEMANTIC RETRIEVAL

摘要：

To bridge the wide semantic gap between the image low-level visual features and the high-level concept conveyed from images is still a challenging job. In this paper, an image semantic representation model (ISRM) was proposed based on statistical learning theory and smooth support vector regression (SSVR). This model is a five-tuple which consists of primitive image set, image feature set, image semantic set, semantic rule set and semantic mappings. The example-based high-level semantic retrieval algorithm (EHSR) and the text-based high-level semantic retrieval algorithm (THSR) for image retrieval using high-level semantic content were designed and implemented respectively. The performance of an experimental image retrieval system constructed according to aforementioned approaches was evaluated on a database of around 3000 images. The experimental results show that ISRM model and EHSR and THSR algorithms are effective in describing image high-level semantic content and can provide flexible and efficient image retrieval performance.

关键词： Content-based image retrieval image semantic model semantic representation SSVR

作者: XIN ZHANG BING WANG ZHI-DE ZHANG XIAO-YAN ZHAO

作者单位: College of Electronics and Information Engineering, Hebei University, Baoding 071002, China College of Mathematics and Computer Science, Hebei University, Baoding, 071002, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 2607-2611

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）