Multi-feature fusion with SVM classification for crime scene investigation image retrieval

Crime scene investigation (CSI) images are important clues for the police to solve cases. With the available of large scale CSI image database, effective and efficient CSI image retrieval becomes more and more important. The main contribution of this paper includes: (1) a DCT domain texture feature extraction algorithm is constructed for effective description of CSI images; (2) GIST descriptor is first exploited in the description of CSI images and integrated with color histogram and the DCT domain texture feature as a fused feature, which describes CSI images from different view including color, texture, and scene structure; (3) SVM Classification technique is used in CSI image retrieval. Experimental results on real CSI image data show that the fusion feature proposed in this paper can well describe the content of CSI images, with an average 15.3% increment in retrieval precision compared with all the single-feature-based algorithm. Using this fusion feature to further train the SVM classifier in the retrieval process, the precision is further improved by 3.1%.