A Novel Retrieval Refinement and Interaction Pattern by Exploring Result Correlations for Image Retrieval

Authors:
Rongrong Ji;Hongxun Yao;Shaohui Liu;Jicheng Wang;Pengfei Xu
Affiliations:
VILAB, School of Computer Science, Harbin Institute of Technology, Harbin, China 150001;VILAB, School of Computer Science, Harbin Institute of Technology, Harbin, China 150001;VILAB, School of Computer Science, Harbin Institute of Technology, Harbin, China 150001;VILAB, School of Computer Science, Harbin Institute of Technology, Harbin, China 150001;VILAB, School of Computer Science, Harbin Institute of Technology, Harbin, China 150001
Venue:
Adaptive Multimedial Retrieval: Retrieval, User, and Semantics
Year:
2007

Citing 14
Cited 0

Content-Based Image Retrieval at the End of the Early Years

IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machine active learning for image retrieval

MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
An approach to automatic classification of text for information retrieval

Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
Document clustering with cluster refinement and model selection capabilities

SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Image Indexing Using Color Correlograms

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Probability Estimates for Multi-class Classification by Pairwise Coupling

The Journal of Machine Learning Research
Asymmetric Bagging and Random Subspace for Support Vector Machines-Based Relevance Feedback in Image Retrieval

IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster-driven refinement for content-based digital image retrieval

IEEE Transactions on Multimedia
Direct kernel biased discriminant analysis: a new content-based image retrieval relevance feedback algorithm

IEEE Transactions on Multimedia
A Framework for Medical Image Retrieval Using Machine Learning and Statistical Similarity Matching Techniques With Relevance Feedback

IEEE Transactions on Information Technology in Biomedicine
Relevance feedback in content-based image retrieval: Bayesian framework, feature subspaces, and progressive learning

IEEE Transactions on Image Processing
CLUE: cluster-based retrieval of images by unsupervised learning

IEEE Transactions on Image Processing
Relevance feedback: a power tool for interactive content-based image retrieval

IEEE Transactions on Circuits and Systems for Video Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

Efficient retrieval of image database that contains multiple predefined categories (e.g. medical imaging databases, museum painting collections) poses significant challenges and commercial prospects. By exploring category correlations of retrieval results in such scenario, this paper presents a novel retrieval refinement and feedback framework. It provides users a novel perceptual-similar interaction pattern for topic-based image retrieval. Firstly, we adopts Pairwise-Coupling SVM (PWC-SVM) to classify retrieval results into predefined image categories, and reorganizes them into category based browsing topics. Secondly, in feedback interaction, category operation is supported to capture users' retrieval purpose fast and efficiently, which differs from traditional relevance feedback patterns that need elaborate image labeling. Especially, an Asymmetry Bagging SVM (ABSVM) network is adopted to precisely capture users' retrieval purpose. And user interactions are accumulated to reinforce our inspections of image database. As demonstrated in experiments, remarkable feedback simplifications are achieved comparing to traditional interaction patterns based on image labeling. And excellent feedback efficiency enhancements are gained comparing to traditional SVM-based feedback learning methods.