Categorization and Reorientation of Images Based on Low Level Features

A hierarchical system to perform automatic categorization and reorientation of images using content analysis is presented. 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 {0 degree , 90 degree , 180 degree , and 270 degree } 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 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.