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

  • Authors:

  • Nikita Orlov;Lior Shamir;Tomasz Macura;Josiah Johnston;D. Mark Eckley;Ilya G. Goldberg

  • Affiliations:

  • Image Informatics and Computational Biology Unit, Laboratory of Genetics, NIA, NIH, 333 Cassell Dr., Suite 3000, Baltimore, MD 21224, United States;Image Informatics and Computational Biology Unit, Laboratory of Genetics, NIA, NIH, 333 Cassell Dr., Suite 3000, Baltimore, MD 21224, United States;Image Informatics and Computational Biology Unit, Laboratory of Genetics, NIA, NIH, 333 Cassell Dr., Suite 3000, Baltimore, MD 21224, United States and Computer Laboratory, University of Cambridge ...;Image Informatics and Computational Biology Unit, Laboratory of Genetics, NIA, NIH, 333 Cassell Dr., Suite 3000, Baltimore, MD 21224, United States;Image Informatics and Computational Biology Unit, Laboratory of Genetics, NIA, NIH, 333 Cassell Dr., Suite 3000, Baltimore, MD 21224, United States;Image Informatics and Computational Biology Unit, Laboratory of Genetics, NIA, NIH, 333 Cassell Dr., Suite 3000, Baltimore, MD 21224, United States

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2008

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Abstract

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 task-specific 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 performance on a variety of classification problems is attributed to (i) a large set of features extracted from images; and (ii) an effective feature selection and weighting algorithm sensitive to specific image classification problems. The algorithms are available for free download from http://www.openmicroscopy.org.