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Color Invariants for Object Recognition

Chapter

Abstract

Color is a very important cue for object recognition, which can help increase the discriminative power of an object-recognition system and also make it more robust to variations in the lighting and imaging conditions. Nonetheless, even though most image acquisition devices provide color data, a lot of object-recognition systems rely solely on simple grayscale information. Part of the reason for this is that although color has advantages, it also introduces some complexities. In particular, the RGB values of a digital color image are only indirectly related to the surface “color” of an object, which depends not only on the object’s surface reflectance but also on such factors as the spectrum of the incident illumination, surface gloss, and the viewing angle. As a result, there has been a great deal of research into color invariants that encode color information but at the same time are insensitive to these other factors. This chapter describes these color invariants, their derivation, and their application to color-based object recognition in detail. Recognizing objects using a simple global image matching strategy is generally not very effective since usually an image will contain multiple objects, involve occlusions, or be captured from a different viewpoint or under different lighting conditions than the model image. As a result, most object-recognition systems describe the image content in terms of a set of local descriptors—SIFT, for example—that describe the regions around a set of detected keypoints. This chapter includes a discussion of the three color-related choices that need to be made when designing an object-recognition system for a particular application: Color-invariance, keypoint detection, and local description. Different object-recognition situations call for different classes of color invariants depending on the particular surface reflectance and lighting conditions that will be encountered. The choice of color invariants is important because there is a trade-off between invariance and discriminative power. All unnecessary invariance is likely to decrease the discriminative power of the system. Consequently, one part of this chapter describes the assumptions underlying the various color invariants, the invariants themselves, and their invariance properties. Then with these color invariants in hand, we turn to the ways in which they can be exploited to find more salient keypoints and to provide richer local region descriptors. Generally but not universally, color has been shown to improve the recognition rate of most object-recognition systems. One reason color improves the performance is that including it in keypoint detection increases the likelihood that the region surrounding the keypoint will contain useful information, so descriptors built around these keypoints tend to be more discriminative. Another reason is that color-invariant-based keypoint detection is more robust to variations in the illumination than grayscale-based keypoint detection. Yet another reason is that local region descriptors based on color invariants more richly characterize the regions, and are more stable relative to the imaging conditions, than their grayscale counterparts.

Keywords

Color-based object recognition Color invariants Keypoint detection SIFT Local region descriptors Illumination invariance Viewpoint invariance Color ratios Shadow invariance. 

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Laboratory Hubert Curien, UMR CNRS 5516Jean Monnet UniversitySaint-EtienneFrance
  2. 2.School of Computing ScienceSimon Fraser UniversityBurnabyCanada

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