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Local Grayvalue Invariants for Image Retrieval
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Feature Detection with Automatic Scale Selection
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Color by Correlation: A Simple, Unifying Framework for Color Constancy
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Saliency, Scale and Image Description
International Journal of Computer Vision
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
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Focus-of-Attention from Local Color Symmetries
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Moment invariants for recognition under changing viewpoint and illumination
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International Journal of Computer Vision
International Journal of Computer Vision
CSIFT: A SIFT Descriptor with Color Invariant Characteristics
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The challenge problem for automated detection of 101 semantic concepts in multimedia
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Pattern Recognition Letters
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Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
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ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
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Computer Vision and Image Understanding
Comparative analysis of color- and grayscale-based feature descriptions for image recognition
Pattern Recognition and Image Analysis
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Machine Vision and Applications
New color GPHOG descriptors for object and scene image classification
Machine Vision and Applications
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In this paper, we compare local colour descriptors to grey-value descriptors. We adopt the evaluation framework of Mikolayzcyk and Schmid. We modify the framework in several ways. We decompose the evaluation framework to the level of local grey-value invariants on which common region descriptors are based. We compare the discriminative power and invariance of grey-value invariants to that of colour invariants. In addition, we evaluate the invariance of colour descriptors to photometric events such as shadow and highlights. We measure the performance over an extended range of common recording conditions including significant photometric variation. We demonstrate the intensity-normalized colour invariants and the shadow invariants to be highly distinctive, while the shadow invariants are more robust to both changes of the illumination colour, and to changes of the shading and shadows. Overall, the shadow invariants perform best: they are most robust to various imaging conditions while maintaining discriminative power. When plugged into the SIFT descriptor, they show to outperform other methods that have combined colour information and SIFT. The usefulness of C-colour-SIFT for realistic computer vision applications is illustrated for the classification of object categories from the VOC challenge, for which a significant improvement is reported.