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B. Scholkopf and A. J. Smola, “Learning with Kernels : Support Vector Machines, Regularization, Optimization, and Beyond,” The MIT Press, Cambridge, 2001.

has been cited by the following article:

• TITLE: Categorization and Reorientation of Images Based on Low Level Features

  AUTHORS: Rajen Bhatt, Gaurav Sharma, Abhinav Dhall, Naresh Kumar, Santanu Chaudhury

  KEYWORDS: Categorization, Digital Content Management, Feature Selection, Orientation Detection, Scalable Boosting, Support Vector Machines

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.3 No.1, February 24, 2011

  ABSTRACT: A hierarchical system to perform automatic categorization and reorientation of images using content analysis is pre-sented. The proposed system first categorizes images to some a priori defined categories using rotation invariant features. At the second stage, it detects their correct orientation out of {0o, 90o, 180o, and 270o} using category specific model. The system has been specially designed for embedded devices applications using only low level color and edge features. Machine learning algorithms optimized to suit the embedded implementation like support vector machines (SVMs) and scalable boosting have been used to develop classifiers for categorization and orientation detection. Results are presented on a collection of about 7000 consumer images collected from open resources. The proposed system finds it applications to various digital media products and brings pattern recognition solutions to the consumer electronics domain.