WND-CHARM: Multi-purpose image classification using compound image transforms

We describe a multi-purpose image classifier that can be applied to a wide variety of image classification tasks without modifications or fine-tuning, and yet provide classification accuracy comparable to state-of-the-art taskspecific image classifiers. The proposed image classifier first extracts a large set of 1025 image features including polynomial decompositions, high contrast features, pixel statistics, and textures. These features are computed on the raw image, transforms of the image, and transforms of transforms of the image. The feature values are then used to classify test images into a set of pre-defined image classes. This classifier was tested on several different problems including biological image classification and face recognition. Although we cannot make a claim of universality, our experimental results show that this classifier performs as well or better than classifiers developed specifically for these image classification tasks. Our classifier's high perform...

• Type: Journal
• Year: 2008
• Where: PRL
• Authors: Nikita Orlov, Lior Shamir, Tomasz J. Macura, Josiah Johnston, D. Mark Eckley, Ilya G. Goldberg
• Keywords: Image, Image Classification, Image Classifier, PRL 2008