Integrating object ontology and region semantic template for crime scene investigation image retrieval
Crime Scene Investigation (CSI) image retrieval plays an important role in solving crimes by providing useful clues for the police force. However, there has been little work done in this area due to limited public data access by researchers. Tested on real-world CSI images, it was observed that existing content-based image retrieval (CBIR) methods do not necessarily retrieve as effectively on CSI image database as compared to other general image databases. Hence, it is important to design CBIR algorithm tuned to CSI image database. This paper proposes a region-based semantic learning method based on object ontology which associates image categories with ‘objects’ in CSI images. Each object corresponds to a pre-defined semantic template (ST) which is defined as the average color and texture feature of a set of sample regions. In this way, low-level features of each region in a CSI image can be converted to an ‘object’ by comparing the region features with the set of pre-defined STs. The ‘objects’ in an image categorize the image based on the object ontology. The above process is referred to as ‘On-Set’. To further improve retrieval performance of On-Set, a weighting strategy named object-frequency-based weighting (OFW) is designed inspired by the idea of term frequency-inverse document frequency (TF-IDF). In OFW, heavier weight is assigned to regions that appear more often in one class and less often in other classes. Experimental results on real-world image data proved the effectiveness of the proposed method for CSI image database retrieval.