Fusion of Structural and Color Local Descriptors for Enhanced Object Recognition

In this paper we study the behavior of local descriptor object recognition methods with respect to 3D geometric transformations and image resolution variations. As expected performance decreases with accentuated perspective and decrease in resolution. To improve performance and robustness, we propose a scheme to fuse color and gradient local descriptors. This approach is motivated by the discriminative power of color in man-made object recognition. The problem of color feature extraction is addressed as well as the considerations on the fusion process and steps to train such fusion. We used SOIL-47A database for experiments and shown a 7\% to 10\% relative improvement when compared with state-of-the-art gradient based descriptors.


Published in:
Proceedings IEEE WIAMIS 2004(5th International Workshop on Image Analysis for Multimedia Interactive Services), 21-23 April, 2004, Lisboa, Portugal
Presented at:
Proceedings IEEE WIAMIS 2004(5th International Workshop on Image Analysis for Multimedia Interactive Services), 21-23 April, 2004, Lisboa, Portugal
Year:
2004
Keywords:
Note:
IDIAP-RR 03-71
Laboratories:




 Record created 2006-03-10, last modified 2018-03-17

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