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Coloring local feature extraction

Joost van de Weijer 1 Cordelia Schmid 1, * 
* Corresponding author
1 LEAR - Learning and recognition in vision
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : Although color is commonly experienced as an indispensable quality in describing the world around us, state-of-the art local feature-based representations are mostly based on shape description, and ignore color information. The description of color is hampered by the large amount of variations which causes the measured color values to vary significantly. In this paper we aim to extend the description of local features with color information. To accomplish a wide applicability of the color descriptor, it should be robust to : 1. photometric changes commonly encountered in the real world, 2. varying image quality, from high quality images to snap-shot photo quality and compressed internet images. Based on these requirements we derive a set of color descriptors. The set of proposed descriptors are compared by extensive testing on multiple applications areas, namely, matching, retrieval and classification, and on a wide variety of image qualities. The results show that color descriptors remain reliable under photometric and geometrical changes, and with decreasing image quality. For all experiments a combination of color and shape outperforms a pure shape-based approach.
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Joost van de Weijer, Cordelia Schmid. Coloring local feature extraction. European Conference on Computer Vision (ECCV '06), May 2006, Graz, Austria. pp.334--348, ⟨10.1007/11744047_26⟩. ⟨inria-00548576⟩



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