International Journal of Computer Vision
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semantic content analysis and annotation of histological images
Computers in Biology and Medicine
Histopathology Image Classification Using Bag of Features and Kernel Functions
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
Visual Pattern Analysis in Histopathology Images Using Bag of Features
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Online Learning for Matrix Factorization and Sparse Coding
The Journal of Machine Learning Research
Design of a medical image database with content-based retrieval capabilities
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
NMF-based multimodal image indexing for querying by visual example
Proceedings of the ACM International Conference on Image and Video Retrieval
ImageCLEF: Experimental Evaluation in Visual Information Retrieval
ImageCLEF: Experimental Evaluation in Visual Information Retrieval
Histopathological image classification using stain component features on a pLSA model
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Design and analysis of a content-based pathology image retrieval system
IEEE Transactions on Information Technology in Biomedicine
Overview of the second workshop on medical content---based retrieval for clinical decision support
MCBR-CDS'11 Proceedings of the Second MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
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Large on-line collections of biomedical images are becoming more common and may be a potential source of knowledge. An important unsolved issue that is actively investigated is the efficient and effective access to these repositories. A good access strategy demands an appropriate indexing of the collection. This paper presents a new method for indexing histology images using multimodal information, taking advantage of two kinds of data: visual data extracted directly from images and available text data from annotations performed by experts. The new strategy called Non-negative Semantic Embedding defines a mapping between visual an text data assuming that the latent space spanned by text annotations is good enough representation of the images semantic. Evaluation of the proposed method is carried out by comparing it with other strategies, showing a remarkable image search improvement since the proposed approach effectively exploits the image semantic relationships.