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Colour Image Retrieval and Object Recognition Using the Multimodal Neighbourhood Signature
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Colour Image Retrieval and Object Recognition Using the Multimodal Neighbourhood Signature
Jiri George Matas4, 5  , Dimitri Koubaroulis4 and Josef Kittler4
| (4) |
CVSSP, University of Surrey, Guildford, Surrey, GU2 7XH, UK |
| (5) |
CMP, Czech Technical University, 121 35 Prague, Czech Republic |
Abstract
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.
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