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. 2008 Jan;29(11):1684-1693.
doi: 10.1016/j.patrec.2008.04.013.

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

Nikita Orlov  1 , Lior Shamir, Tomasz Macura, Josiah Johnston, D Mark Eckley, Ilya G Goldberg
Affiliations

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

Nikita Orlov et al. Pattern Recognit Lett. 2008 Jan.

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 openmicroscopy.org.

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Figures

Fig. 1
Fig. 1
The construction of the feature vector.
Fig. 2
Fig. 2
The effect of the exponent value – p on the accuracy of five image classification problems.
Fig. 3
Fig. 3
Typical images of the 10 classes of the HeLa dataset
Fig. 4
Fig. 4
Typical images of pollen grains of the 7 classes of the Pollen dataset
Fig. 5
Fig. 5
Typical images of the 5 classes of the CHO dataset (giant, hoechst, lamp2, nop4, tubulin)
Fig. 6
Fig. 6
Comparison of Fisher scores assigned to the 1025 features in different image classification problems
See this image and copyright information in PMC

References

    1. Awate SP, Tasdizen T, Foster N, Whitaker RT. Adaptive Markov modeling for mutual-information-based, unsupervised MRI brain-tissue classification. Medical Image Analysis. 2006;10:726–739. - PubMed
    1. Belhumeur PN, Hespanha JP, Kriegman DJ. Eigenfaces vs. fisherface: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1997;19:711–720.
    1. Bishop CM. Pattern Recognition and Machine Learning. Springer Press; 2006.
    1. Boland M, Markey M, Murphy R. Automated recognition of patterns characteristic of subcellular structures in fluorescence microscopy images. Cytometry. 1998;33:366–375. - PubMed
    1. Boland MV, Murphy RF. A Neural Network Classifier Capable of Recognizing the Patterns of all Major Subcellular Structures in Fluorescence Microscope Images of HeLa Cells. Bioin-formatics. 2001;17:1213–1223. - PubMed