The nature of statistical learning theory
The nature of statistical learning theory
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploratory image databases: content-based retrieval
Exploratory image databases: content-based retrieval
Indoor-Outdoor Image Classification
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Global semantic classification of scenes using power spectrum templates
IM'99 Proceedings of the 1999 international conference on Challenge of Image Retrieval
IEEE Transactions on Circuits and Systems for Video Technology
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This paper deals with knowledge extraction from visual data for content-based image retrieval of natural scenes. Images are analysed using a ridgelet transform that enhances information at different scales, orientations and spatial localizations. The main contribution of this work is to propose a method that reduces the size and the redundancy of this ridgelet representation, by defining both global and local signatures that are specifically designed for semantic classification and content-based retrieval. An effective recognition system can be built when these descriptors are used in conjunction with a support vector machine (SVM). Classification and retrieval experiments are conducted on natural scenes, to demonstrate the effectiveness of the approach.