Texture features for content-based retrieval
Principles of visual information retrieval
Digital Image Processing
Suitability of texture features to assess changes in trabecular bone architecture
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Better Rules, Few Features: A Semantic Approach to Selecting Features from Text
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Hidden Markov models for automatic annotation and content-based retrieval of images and video
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A Probabilistic Semantic Model for Image Annotation and Multi-Modal Image Retrieva
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dual cross-media relevance model for image annotation
Proceedings of the 15th international conference on Multimedia
Automatic medical image annotation in ImageCLEF 2007: Overview, results, and discussion
Pattern Recognition Letters
IWDM'06 Proceedings of the 8th international conference on Digital Mammography
Comparison between wolfe, boyd, BI-RADS and tabár based mammographic risk assessment
IWDM'06 Proceedings of the 8th international conference on Digital Mammography
Classifying color edges in video into shadow-geometry, highlight, or material transitions
IEEE Transactions on Multimedia
Image classification for content-based indexing
IEEE Transactions on Image Processing
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Due to the increasing use of digital medical images, a need exists to develop an approach for automatic image annotation, which provides textual labels for images. Thus added labels can be used to access images using textual queries. Automatic image annotation can be separated into two individual tasks: feature extraction and image classification. In this paper, the authors present feature extraction methods for calcification mammograms. The resultant features, based on BI-RADS standards, make annotated image contents represent the correct medical meaning and tag correspondent terms. Furthermore, this paper also proposes a probabilistic SVM approach to image classification. Finally, the experimental results indicate that the probabilistic SVM approach to image annotation can achieve 79.5% in the average accuracy rate.