International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
A comparison of SVM and HMM classifiers in the off-line signature verification
Pattern Recognition Letters
A novel framework for SVM-based image retrieval on large databases
Proceedings of the 13th annual ACM international conference on Multimedia
Similarity learning via dissimilarity space in CBIR
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Kernel-based distance metric learning for content-based image retrieval
Image and Vision Computing
Negative Samples Analysis in Relevance Feedback
IEEE Transactions on Knowledge and Data Engineering
An in-memory relevance feedback technique for high-performance image retrieval systems
Proceedings of the 6th ACM international conference on Image and video retrieval
Region-based image retrieval using an object ontology and relevance feedback
EURASIP Journal on Applied Signal Processing
Unified framework for fast exact and approximate search in dissimilarity spaces
ACM Transactions on Database Systems (TODS)
Intra-dimensional feature diagnosticity in the Fuzzy Feature Contrast Model
Image and Vision Computing
Adaptive multiple feedback strategies for interactive video search
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Semantic image classification using statistical local spatial relations model
Multimedia Tools and Applications
Ensemble one-class support vector machines for content-based image retrieval
Expert Systems with Applications: An International Journal
Pattern Recognition
Top-Down Approach to Image Similarity Measures
ICCVG 2008 Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers
Automated diagnosis of Alzheimer's disease using image similarity and user feedback
Proceedings of the ACM International Conference on Image and Video Retrieval
IEEE Transactions on Image Processing
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Biased discriminant euclidean embedding for content-based image retrieval
IEEE Transactions on Image Processing
Dayside corona aurora classification based on X-gray level aura matrices
Proceedings of the ACM International Conference on Image and Video Retrieval
Similarity Learning for 3D Object Retrieval Using Relevance Feedback and Risk Minimization
International Journal of Computer Vision
Online learning of relevance feedback from expert readers for mammogram retrieval
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Random sampling based SVM for relevance feedback image retrieval
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Towards hierarchical context: unfolding visual community potential for interactive video retrieval
Multimedia Tools and Applications
Exploring latent class information for image retrieval using the bag-of-feature model
MM '11 Proceedings of the 19th ACM international conference on Multimedia
A relevance feedback approach for content based image retrieval using gaussian mixture models
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Proceedings of the 20th ACM international conference on Multimedia
Proceedings of the 20th ACM international conference on Multimedia
Online multi-modal distance learning for scalable multimedia retrieval
Proceedings of the sixth ACM international conference on Web search and data mining
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A new scheme of learning similarity measure is proposed for content-based image retrieval (CBIR). It learns a boundary that separates the images in the database into two clusters. Images inside the boundary are ranked by their Euclidean distances to the query. The scheme is called constrained similarity measure (CSM), which not only takes into consideration the perceptual similarity between images, but also significantly improves the retrieval performance of the Euclidean distance measure. Two techniques, support vector machine (SVM) and AdaBoost from machine learning, are utilized to learn the boundary. They are compared to see their differences in boundary learning. The positive and negative examples used to learn the boundary are provided by the user with relevance feedback. The CSM metric is evaluated in a large database of 10009 natural images with an accurate ground truth. Experimental results demonstrate the usefulness and effectiveness of the proposed similarity measure for image retrieval.