Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
IEEE Computer Graphics and Applications
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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
Endomicroscopic image retrieval and classification using invariant visual features
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Histology image indexing using a non-negative semantic embedding
MCBR-CDS'11 Proceedings of the Second MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
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Semantic annotation of microscopical field of views is one of the key problems in computer assistance of histopathological images. In this paper a new method for extracting patch descriptors is proposed and evaluated using a probabilistic latent semantic analysis (pLSA) classification model. The proposed approach is based on the analysis of the different dyes used to stain the histological sample. This analysis allows to find local regions that correspond to cells in the image, which are then described by the SIFT descriptors of the stain components. The proposed approach outperforms the conventional sampling and description strategies, proposed in the literature.