Performance evaluation of local colour invariants
article
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.
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.
Topics
TNO Identifier
28542
Source
Computer Vision and Image Understanding, 113, pp. 48-62.
Pages
48-62
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