A relevance feedback scheme based on Hidden Markov Model Regression 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. For a relevance feedback algorithm to be effective, it must grasp a user's query concept accurately. In this paper, we propose a relevance feedback framework based on Hidden Markov Model Regression (HMMR) in content-based 3D model retrieval systems. Given a 3D model retrieval system, we collect and store user's feedback and use HMMR to enhance the retrieval performances. Experimental results show that this algorithm achieves higher search accuracy than traditional query refinement schemes.