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Image classification: classifying distributions of visual features

 

We classify an image by generating a list of salient visual features present in the luminance channel, and matching the resulting variable-length feature list to category- specific generative models for such features. To facilitate quick computation, we use thresholded Viola-Jones rectangular features, each parameterized by a five-dimensional vector. For each image category, a probability distribution for feature-lists is given by alatent conditional independence (LCI) model and classification is maximum likelihood. On the NIST tax forms database [3], where intra-category variations include variable scan-lightness, skew, noise, and machine-printed form-filling, our method improves performance over published results, while requiring very little training data, and without relying on an extensive set of handcrafted features.

 
citation

Sarkar, P. Image classification: classifying distributions of visual features. 18th International Conference on Pattern Recognition; 2006 September 20-24; Hong Kong; China. Los Alamitos, CA: IEEE Computer Society; 2006; 472-475.