Efficient Image Retrieval via Feature Fusion and Adaptive Weighting

摘要：

　　In the community of content-based image retrieval(CBIR),single feature only describes specific aspect of image content,resulting in false positive matches prevalently returned as candidate retrieval results with low precision and recall.Typically,frequently-used SIFT feature only depicts local gradient distribution within ROIs of gray scale images lacking color information,and tends to produce limited retrieval performance.In order to tackle such problems,we propose a feature fusion method of integrating multiple diverse image features to gain more complementary and helpful image information.Furthermore,to represent the disparate powers of discrimination of image features,a dynamically updating Adaptive Weights Allocation Algorithm(AWAA)which rationally allocates fusion weights proportional to their contributions to matching is proposed in the paper.Extensive experiments on several benchmark datasets demonstrate that feature fusion simultaneously with adaptive weighting based image retrieval yields more accurate and robust retrieval results than conventional retrieval schema.

关键词： Image retrieval Feature fusion Adaptive weighting Color Names BoW

作者: Xiangbin Shi Zhongqiang Guo Deyuan Zhang

作者单位: School of Computer,Shenyang Aerospace University,Shenyang 110136,China;Key Laboratory of Liaoning Ge School of Information,Liaoning University,Shenyang 110036,China School of Computer,Shenyang Aerospace University,Shenyang 110136,China;Key Laboratory of Liaoning Ge

会议类型: 国际会议

会议名称: 第七届全国模式识别学术会议(The 7th Chinese Conference on Pattern Recognition,CCPR2016)

会议地点: 成都

会议语种:英文

页码: 259-273

在线出版日期: 2016-11-03（万方平台首次上网日期，不代表论文的发表时间）