Interactive Object-Based Retrieval Using Relevance Feedback

In this paper we present an interactive, object-based video retrieval system which features a novel query formulation method that is used to iteratively refine an underlying model of the search object. As the user continues query composition and browsing of retrieval results, the system’s object modeling process, based on Gaussian probability distributions, becomes incrementally more accurate, leading to better search results. To make the interactive process understandable and easy to use, a custom user-interface has been designed and implemented that allows the user to interact with segmented objects in formulating a query, in browsing a search result, and in re-formulating a query by selecting an object in the search result.

• Type: Conference
• Year: 2005
• Where: ACIVS
• Authors: Sorin Sav, Hyowon Lee, Noel E. O'Connor, Alan F. Smeaton
• Keywords: ACIVS 2005, Computer Vision, Gaussian Probability Distributions, Object-based Video Retrieval, User Continues Query