基于融合特征的现勘图像检索结果优化算法

Multi-feature Fusion Based Retrieval Results Optimization for Crime Scene Investigation Image Retrieval

刑侦现勘图像数据库是具有保密性高、图像内容罕见等极具行业特色的图像数据库.针对现勘图像内容复杂、目标物体不明确的特点,提出了DCT-DCT波纹理特征,并与HSV颜色直方图特征、GIST特征相融合构成融合特征.与常用的图像特征相比,DCT-DCT波纹理特征能够得到较高的检索效率,而融合特征的平均检索查准率高于构成其本身的三种特征的平均检索查准率.最后,将语义分析技术引入到检索过程中,提出基于检索结果优化的现勘图像检索算法,利用支持向量机(Support Vector Machine,SVM)分类器对查询图像进行语义提取,并对初次检索的结果进行语义分析,根据初检结果中语义类别的占比选择二次检索方案,该算法能在按例查询的基础上进一步提高平均检索查准率.

The image database of crime scene investigation (CSI)has the charact eristics of high confidentiality,rare image content and so on.Aiming at the complexity of the content and the ambiguity of the target object,the DCT-DCT wave texture feature is proposed,which is fused with HSV color histogram feature and GIST feature to form the fusion feature.Compared with the commonly used image features,DCT-DCT wave texture feature can get higher retrieval efficiency,and the average retrieval precision rate of the fused features is higher than that of the three features.Finally,the semantic analysis technology is introduced into the retrieval process,and an image retrieval algorithm based on the optimization of retrieval results is proposed.Support vector machine (SVM)classifier was used to extract the semantic of the query image.The semantic analysis of the results of the first retrieval is carried out,and the second retrieval scheme is selected according to the proportion of semantic categories in the initial retrieval results.The algorithm can further improve the average retrieval accuracy based on case-by-case query.