A Relevance Feedback Based on Bayesian Logistic Regression for 3D Model Retrieval

摘要：

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 users 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 users query concept accurately and quickly. In this paper, we propose a relevance feedback framework based on Bayesian logistic regression in contentbased 3D model retrieval systems to incorporate relevance feedback information. Bayesian logistic regression relevance feedback framework using an active learning algorithm based on variance reduction to actively select documents for user evaluation. Experimental results show that this algorithm achieves higher search accuracy than traditional query refinement schemes.

关键词： 3D model retrieval bayesian logistic regression relevance feedback

作者: Zhang Zhi-yong Yang Bai-lin

作者单位: Department of Computer and Electronic Engineering Zhejiang Gongshang University Hangzhou China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 77-80

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）