<![CDATA[Due to the recent improvements in digital photography and storage capacity, storing large amounts of images has been made possible. Consequently efficient means to retrieve images matching a user’s query are needed. In this paper, we propose a framework based on a bipartite graph model (BGM) for semantic image retrieval. BGM is a scalable data structure that aids semantic indexing in an efficient manner, and it can also be incrementally updated. Firstly, all the images are segmented into several regions with image segmentation algorithm, pre-trained SVMs are used to annotate each region, and final label is obtained by merging all the region labels. Then we use the set of images and the set of region labels to build a bipartite graph. When a query is given, a query node, initially containing a fixed number of labels, is created to attach to the bipartite graph. The node then distributes the labels based on the edge weight between the node and its neighbors. Image nodes receiving the most labels represent the most relevant images. Experimental results demonstrate that our proposed technique is promising.DOI: http://dx.doi.org/10.11591/telkomnika.v11i11.3588]]>
