International Journal of Computer Vision
Reflectance based object recognition
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Is Machine Colour Constancy Good Enough?
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
FOCUS: Searching for Multi-colored Objects in a Diverse Image Database
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Comparing Images under Variable Illumination
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Integrated spatial and feature image query
Multimedia Systems
Mean Shift Analysis and Applications
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Recognizing 3D Object Using Photometric Invariant
Recognizing 3D Object Using Photometric Invariant
Comparing Intensity Transformations and Their Invariants in the Context of Color Pattern Recognition
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Fusion of Multiple Cue Detectors for Automatic Sports Video Annotation
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Hierarchical decision making scheme for sports video categorisation with temporal post-processing
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Coloring local feature extraction
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Reconstruction of low-resolution images using adaptive bimodal priors
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
A weighted dominant color descriptor for content-based image retrieval
Journal of Visual Communication and Image Representation
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A novel approach to colour-based object recognition and image retrieval -the multimodal neighbourhood signature- is proposed. Object appearance is represented by colour-based features computed from image neighbourhoods with multi-modal colour density function. Stable invariants are derived from modes of the density function that are robustly located by the mean shift algorithm. The problem of extracting local invariant colour features is addressed directly, without a need for prior segmentation or edge detection. The signature is concise - an image is typically represented by a few hundred bytes, a few thousands for very complex scenes. The algorithm's performance is first tested on a region-based image retrieval task achieving a good (92%) hit rate at a speed of 600 image comparisons per second. The method is shown to operate successfully under changing illumination, viewpoint and object pose, as well as non-rigid object deformation, partial occlusion and the presence of background clutter dominating the scene. The performance of the multimodal neighbourhood signature method is also evaluated on a standard colour object recognition task using a publicly available dataset. Very good recognition performance (average match percentile 99.5%) was achieved in real time (average 0.28 seconds for recognising a single image) which compares favourably with results reported in the literature.