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Coarse Adaptive Color Image Segmentation for Visual Object Classification

Alain Pujol 1 Liming Chen 1 
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : This paper deals with perceptually inspired image segmentation for the purpose of generic image classification or object detection. Indeed, in our algorithm we will try to stay true to human perception and more specifically Gestalt Theory. Input images are processed in a three-step framework: a pre-processing step where the image is filtered and where perceptually similar colors are grouped as per color constancy law, a clustering step where we also determine an optimal number of quantized colors and a post processing step where we add spatial information and merge smaller regions as per “good continuation” and proximity laws. Another major feature of our algorithm is that it adapts to image dynamics and doesn’t require image-specific parameter tuning. Application on a 10,000 image dataset shows the algorithm succeeds in producing large coarse regions that can be used for feature extraction.
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Submitted on : Tuesday, April 4, 2017 - 8:52:45 AM
Last modification on : Tuesday, June 1, 2021 - 2:08:09 PM


  • HAL Id : hal-01501223, version 1


Alain Pujol, Liming Chen. Coarse Adaptive Color Image Segmentation for Visual Object Classification. 15th International Conference on Systems, Signals and Image Processing, Jun 2008, Bratislava, Slovakia. pp.1-4. ⟨hal-01501223⟩



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