LIU Ying, HU Dan, FAN Jiu-lun, et al. Multi-feature Fusion Based Retrieval Results Optimization for Crime Scene Investigation Image Retrieval[J]. Acta Electronica Sinica, 2019, 47(2): 296-301.
LIU Ying, HU Dan, FAN Jiu-lun, et al. Multi-feature Fusion Based Retrieval Results Optimization for Crime Scene Investigation Image Retrieval[J]. Acta Electronica Sinica, 2019, 47(2): 296-301. DOI: 10.3969/j.issn.0372-2112.2019.02.006.
The image database of crime scene investigation (CSI)has the characteristics 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.