A knowledge-based semantic approach for image collection summarization

With the advent of digital cameras, the number of digital images is on the increase. As a result, image collection summarization systems are proposed to provide users with a condense set of summary images as a representative set to the original high volume image set. In this paper, a semantic knowledge-based approach for image collection summarization is presented. Despite ontology and knowledge-based systems have been applied in other areas of image retrieval and image annotation, most of the current image summarization systems make use of visual or numeric metrics for conducting the summarization. Also, some image summarization systems jointly model visual data of images together with their accompanying textual or social information, while these side data are not available out of the context of web or social images. The main motivation of using ontology approach in this study is its ability to improve the result of computer vision tasks by the additional knowledge which it provides to the system. We defined a set of ontology based features to measure the amount of semantic information contained in each image. A semantic similarity graph was made based on semantic similarities. Summary images were then selected based on graph centrality on the similarity graph. Experimental results showed that the proposed approach worked well and outperformed the current image summarization systems.