Colour Interest Points for Image Retrieval

In image retrieval scenarios, many methods use interest point detection at an early stage to find regions in which descriptors are calculated. Finding salient locations in image data is crucial for these tasks. Observing that most current methods use only the luminance information of the images, we investigate the use of colour information in in- terest point detection. Based on the Harris corner detector, a way to use multi-channel images is explored and different colour spaces are evaluated. To determine the characteris- tic scale of an interest point, a new colour scale selection method is presented. We show that using colour informa- tion and boosting salient colours results in improved perfor- mance in retrieval tasks.

[1]  Cordelia Schmid,et al.  An Affine Invariant Interest Point Detector , 2002, ECCV.

[2]  Hans P. Moravec.  Obstacle avoidance and navigation in the real world by a seeing robot rover , 1980 .

[3]  Cordelia Schmid,et al.  Local Grayvalue Invariants for Image Retrieval , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Cordelia Schmid,et al.  Matching images with different resolutions , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[5]  Cordelia Schmid,et al.  Evaluation of Interest Point Detectors , 2000, International Journal of Computer Vision.

[6]  Tony Lindeberg,et al.  Feature Detection with Automatic Scale Selection , 1998, International Journal of Computer Vision.

[7]  Rachid Deriche,et al.  Differential invariants for color images , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[8]  Joost van de Weijer,et al.  Edge and corner detection by photometric quasi-invariants , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  LindebergTony.  Feature Detection with Automatic Scale Selection , 1998 .

[10]  Michael Brady,et al.  Saliency, Scale and Image Description , 2001, International Journal of Computer Vision.

[11]  C. Schmid,et al.  Indexing based on scale invariant interest points , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[12]  B. S. Manjunath,et al.  An axiomatic approach to corner detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[13]  Nicu Sebe,et al.  Evaluation of Intensity and Color Corner Detectors for Affine Invariant Salient Regions , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[14]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.

[15]  Joost van de Weijer,et al.  Boosting color saliency in image feature detection , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[18]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[19]  Tony Lindeberg,et al.  Shape-adapted smoothing in estimation of 3-D shape cues from affine deformations of local 2-D brightness structure , 1997, Image Vis. Comput..

[20]  Arnold W. M. Smeulders,et al.  The Amsterdam Library of Object Images , 2004, International Journal of Computer Vision.

[21]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.