Image and Video Retrieval

We present a user-centric system for visualization and layout for content-based image retrieval and browsing. Image features (visual and/or semantic) are analyzed to display and group retrievals as thumbnails in a 2-D spatial layout which conveys mutual similarities. Moreover, a novel subspace feature weighting technique is proposed and used to modify 2-D layouts in a variety of context-dependent ways. An efficient computational technique for subspace weighting and re-estimation leads to a simple user-modeling framework whereby the system can learn to display query results based on layout examples (or relevance feedback) provided by the user. The resulting retrieval, browsing and visualization engine can adapt to the user's (time-varying) notions of content, context and preferences in style of interactive navigation. Monte Carlo simulations with synthetic "user-layouts" as well as pilot user studies have demonstrated the ability of this framework to accurately model or "mimic" users by automatically generating layouts according to their preferences.