A Log-Based Relevance Feedback Scheme for 3D Model Retrieval

Abstract

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, retrieval system can take advantage of the log data to enhance its retrieval performance. In this paper, we propose a unified framework for log-based relevance feedback that integrates the log of feedback data into the traditional relevance feedback schemes to learn effectively the correlation between low-level 3D model features and high-level concepts. In this scheme, we present a learning technique for log-based relevance feedback biased on support vector machine. Experimental results show that this log-based scheme achieves higher search accuracy than traditional query refinement schemes.