Using Semantic Graphs for Image Search

Abstract

In this paper, we propose a Semantic Graphs for Image Search (SGIS) system, which provides a novel way for image search by utilizing collaborative knowledge in Wikipedia and network analysis to form semantic graphs for search-term suggestion. The collaborative article editing process of Wikipedia's contributors is formalized as bipartite graphs that are folded into networks between terms. When user types in a search term, SGIS automatically retrieves an interactive semantic graph of related terms that allow users easily find related images not limited to a specific search term. Interactive semantic graph then serves as an interface to retrieve images through existing commercial search engines. This method significantly saves users' time by avoiding multiple search keywords that are usually required in generic search engines. It benefits both naïve user who does not possess a large vocabulary (e.g., students) and professionals who look for images on a regular basis. In our experiments, 85% of the participants favored SGIS system than commercial search engines.