The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Comparing images using joint histograms
Multimedia Systems - Special issue on video content based retrieval
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machine pairwise classifiers with error reduction for image classification
MULTIMEDIA '01 Proceedings of the 2001 ACM workshops on Multimedia: multimedia information retrieval
Comparison of View-Based Object Recognition Algorithms Using Realistic 3D Models
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Target Testing and the PicHunter Bayesian Multimedia Retrieval System
ADL '96 Proceedings of the 3rd International Forum on Research and Technology Advances in Digital Libraries
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Eigenregions for Image Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Content Aware Image Enhancement
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
Region-based annotation of digital photographs
CCIW'11 Proceedings of the Third international conference on Computational color imaging
Semantic awareness for automatic image interpretation
Proceedings of the 20th ACM international conference on Multimedia
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The paper describes an innovative image annotation tool, based on a multi-class Support Vector Machine, for classifying image pixels in one of seven classes – sky, skin, vegetation, snow, water, ground, and man-made structures – or as unknown. These visual categories mirror high-level human perception, permitting the design of intuitive and effective color and contrast enhancement strategies. As a pre-processing step, a smart color balancing algorithm is applied, making the overall procedure suitable for uncalibrated images, such as images acquired by unknown systems under unknown lighting conditions.