DocumentCode :
2130663
Title :
Colour logo and trademark detection in unconstrained images using colour edge gradient co-occurrence histograms
Author :
Phan, Raymond ; Chia, John ; Androutsos, Dimitrios
Author_Institution :
Dept. of Electr. & Comput. Eng., Ryerson Univ., Toronto, ON
fYear :
2008
fDate :
4-7 May 2008
Abstract :
In this paper, we present an extension of the Colour Edge Co-occurence Histogram (CECH) object detection scheme for detecting logos and trademarks in unconstrained colour images. We introduce more accurate information to the CECH by virtue of incorporating colour edge detection using vector order statistics, producing a more accurate representation of edges in images, as compared to the simple colour difference edge classification which is done in the CECH. Our proposed method is thus reliant on edge gradient information, and so we call it the Colour Edge Gradient Co-occurrence Histogram (CEGCH). We also illustrate a colour quantization scheme based in the Hue-Saturation-Value (HSV) colour space, illustrating that it is more suitable for logo and trademark detection in comparison to the colour quantization scheme used with the CECH. Results illustrate that the CEGCH detects logos and trademarks with greater accuracy in comparison to the CECH.
Keywords :
edge detection; gradient methods; image colour analysis; image representation; object detection; statistical analysis; colour edge gradient co-occurrence histograms; image representation; logo detection; object detection; trademark detection; unconstrained colour image; vector order statistics; Content based retrieval; Histograms; Image edge detection; Image retrieval; Object detection; Pixel; Quantization; Solids; Statistics; Trademarks; Colour Edge Gradient Co-occurrence Histogram (CEGCH); HSV quantization; colour edge detection; object detection; pattern recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical and Computer Engineering, 2008. CCECE 2008. Canadian Conference on
Conference_Location :
Niagara Falls, ON
ISSN :
0840-7789
Print_ISBN :
978-1-4244-1642-4
Electronic_ISBN :
0840-7789
Type :
conf
DOI :
10.1109/CCECE.2008.4564591
Filename :
4564591
Link To Document :
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