A Log-Based 3D Model Retrieval Relevance Feedback Scheme Using Biased SVMs

Abstract

Retrieval relevance feedback is an iterative search technique to bridge the semantic gap between the high level user intention and low level data representation. This technique interactively determines a user's desired output or query concept by asking the user whether certain proposed 3D models are relevant or not. In the past, most research efforts in 3D model retrieval field have focused on designing algorithms for traditional relevance feedback. Given a 3D model retrieval system, it can collect and store users’ relevance feedback information in a history log, 3D model retrieval system can take advantage of the log data of users’ feedback to enhance its retrieval performance. In this paper, we propose a unified 3D model retrieval relevance feedback framework that integrates the log data into the traditional relevance feedback schemes to learn effectively the correlation between low-level 3D model features and high-level concepts. In this 3D model retrieval relevance feedback scheme, we use a learning technique for relevance feedback, named biased support vector machine based relevance feedback.  Experimental results show that this log-based scheme can achieves higher search accuracy than traditional query refinement schemes.