Scalable large-margin Mahalanobis distance metric learning
IEEE Transactions on Neural Networks
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Retrieval evaluation and distance learning from perceived similarity between endomicroscopy videos
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Fast neighborhood component analysis
Neurocomputing
A scalable algorithm for learning a mahalanobis distance metric
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Distance metric learning with eigenvalue optimization
The Journal of Machine Learning Research
A boosting approach for supervised Mahalanobis distance metric learning
Pattern Recognition
Boosting multi-kernel locality-sensitive hashing for scalable image retrieval
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Supervised content based image retrieval using radiology reports
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
Directional Binary Wavelet Patterns for Biomedical Image Indexing and Retrieval
Journal of Medical Systems
A robust and efficient doubly regularized metric learning approach
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Regularized soft K-means for discriminant analysis
Neurocomputing
Online multi-modal distance learning for scalable multimedia retrieval
Proceedings of the sixth ACM international conference on Web search and data mining
Multi-metric learning for multi-sensor fusion based classification
Information Fusion
Journal of Information Science
Modular interpretation of low altitude aerial images of non-urban environment
Digital Signal Processing
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Similarity measurement is a critical component in content-based image retrieval systems, and learning a good distance metric can significantly improve retrieval performance. However, despite extensive study, there are several major shortcomings with the existing approaches for distance metric learning that can significantly affect their application to medical image retrieval. In particular, “similarity” can mean very different things in image retrieval: resemblance in visual appearance (e.g., two images that look like one another) or similarity in semantic annotation (e.g., two images of tumors that look quite different yet are both malignant). Current approaches for distance metric learning typically address only one goal without consideration of the other. This is problematic for medical image retrieval where the goal is to assist doctors in decision making. In these applications, given a query image, the goal is to retrieve similar images from a reference library whose semantic annotations could provide the medical professional with greater insight into the possible interpretations of the query image. If the system were to retrieve images that did not look like the query, then users would be less likely to trust the system; on the other hand, retrieving images that appear superficially similar to the query but are semantically unrelated is undesirable because that could lead users toward an incorrect diagnosis. Hence, learning a distance metric that preserves both visual resemblance and semantic similarity is important. We emphasize that, although our study is focused on medical image retrieval, the problem addressed in this work is critical to many image retrieval systems. We present a boosting framework for distance metric learning that aims to preserve both visual and semantic similarities. The boosting framework first learns a binary representation using side information, in the form of labeled pairs, and then computes the distance as a weighted Hamming distance using the learned binary representation. A boosting algorithm is presented to efficiently learn the distance function. We evaluate the proposed algorithm on a mammographic image reference library with an Interactive Search-Assisted Decision Support (ISADS) system and on the medical image data set from ImageCLEF. Our results show that the boosting framework compares favorably to state-of-the-art approaches for distance metric learning in retrieval accuracy, with much lower computational cost. Additional evaluation with the COREL collection shows that our algorithm works well for regular image data sets.