The multi-class metric problem in nearest neighbour discrimination rules
Pattern Recognition
Trading MIPS and memory for knowledge engineering
Communications of the ACM
Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic feature relevance learining for content-based image retrieval
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Independent feature analysis for image retrieval
Pattern Recognition Letters - Special issue on machine learning and data mining in pattern recognition
Edge-based structural features for content-based image retrieval
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
Feature normalization and likelihood-based similarity measures for image retrieval
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
Extraction of feature subspaces for content-based retrieval using relevance feedback
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Machine Learning
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Relevance Feedback Decision Trees in Content-Based Image Retrieval
CBAIVL '00 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'00)
Exploring the Nature and Variants of Relevance Feedback
CBAIVL '01 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'01)
Strategies for Positive and Negative Relevance Feedback in Image Retrieval
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Feature Relevance Learning with Query Shifting for Content-Based Image Retrieval
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Gabor wavelet representation for 3-D object recognition
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
Kernel VA-files for relevance feedback retrieva
MMDB '03 Proceedings of the 1st ACM international workshop on Multimedia databases
Relevance feedback: perceptual learning and retrieval in bio-computing, photos, and video
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Negative Samples Analysis in Relevance Feedback
IEEE Transactions on Knowledge and Data Engineering
Mixture of KL subspaces for relevance feedback
Multimedia Tools and Applications
Performance evaluation of relevance feedback methods
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
A framework of CBIR system based on relevance feedback
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Computer Vision and Image Understanding
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Relevance feedback methods for content-based image retrieval have shown promise in a variety of image database applications. These techniques assume two-class relevance feedback: relevant and irrelevant classes. While simple computationally, two-class relevance feedback often becomes inadequate in providing sufficient information to help rapidly improve retrieval performance. In this paper we propose a multi-class form of relevance feedback retrieval to try to exploit multi-class information. For a given query, we use a χ2 analysis to determine the local relevance of each feature dimension with multi-class relevance feedback. This information is then used to customize the retrieval metric to rank images. By exploiting multiclass information, our method is able to create flexible metrics that better capture user perceived similarity. In a number of image data sets, the method achieves a higher level of precision with fewer iterations, demonstrating the potential for substantial improvements over two-class relevance feedback retrieval.