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Proceedings Paper

Pet fur color and texture classification
Author(s): Jonathan Yen; Debarghar Mukherjee; SukHwan Lim; Daniel Tretter
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Paper Abstract

Object segmentation is important in image analysis for imaging tasks such as image rendering and image retrieval. Pet owners have been known to be quite vocal about how important it is to render their pets perfectly. We present here an algorithm for pet (mammal) fur color classification and an algorithm for pet (animal) fur texture classification. Per fur color classification can be applied as a necessary condition for identifying the regions in an image that may contain pets much like the skin tone classification for human flesh detection. As a result of the evolution, fur coloration of all mammals is caused by a natural organic pigment called Melanin and Melanin has only very limited color ranges. We have conducted a statistical analysis and concluded that mammal fur colors can be only in levels of gray or in two colors after the proper color quantization. This pet fur color classification algorithm has been applied for peteye detection. We also present here an algorithm for animal fur texture classification using the recently developed multi-resolution directional sub-band Contourlet transform. The experimental results are very promising as these transforms can identify regions of an image that may contain fur of mammals, scale of reptiles and feather of birds, etc. Combining the color and texture classification, one can have a set of strong classifiers for identifying possible animals in an image.

Paper Details

Date Published: 29 January 2007
PDF: 8 pages
Proc. SPIE 6508, Visual Communications and Image Processing 2007, 65081X (29 January 2007); doi: 10.1117/12.703227
Show Author Affiliations
Jonathan Yen, Toshiba America (United States)
Debarghar Mukherjee, Hewlett Packard Labs. (United States)
SukHwan Lim, Hewlett Packard Labs. (United States)
Daniel Tretter, Hewlett Packard Labs. (United States)

Published in SPIE Proceedings Vol. 6508:
Visual Communications and Image Processing 2007
Chang Wen Chen; Dan Schonfeld; Jiebo Luo, Editor(s)

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