publication . Conference object . 2007

SIFT-CCH: Increasing the SIFT distinctness by Color Co-occurrence Histograms

Cosmin Ancuti; Philippe Bekaert;
Open Access English
  • Published: 01 Jan 2007
  • Publisher: IEEE
  • Country: Belgium
Abstract
Describing regions in a distinctive way, in order to find correct correspondences in images of two separated views, represents a complex and essential task of computer vision. Until now, SIFT (Scale Invariant Feature Transform) has been proven to be the most reliable descriptor among the others. One of the main drawbacks of SIFT is its vulnerability to color images, being designed mainly for the gray images. To overcome this problem and also to increase the overall distinctness of the SIFT in this paper we introduce a new descriptor that combines the SIFT approach with the color co-occurrence histograms (CCH), a concept used extensively in color texture retrieva...
Subjects
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Artificial intelligence, business.industry, business, Scale-invariant feature transform, Cognitive neuroscience of visual object recognition, GLOH, Histogram, Image retrieval, Image texture, Feature extraction, Co-occurrence, Computer vision, Pattern recognition, Mathematics
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publication . Conference object . 2007

SIFT-CCH: Increasing the SIFT distinctness by Color Co-occurrence Histograms

Cosmin Ancuti; Philippe Bekaert;