Content-based retrieval of logo and trademarks in unconstrained color image databases using Color Edge Gradient Co-occurrence Histograms

• Published in: Computer vision and image understanding 2010, Vol.114 (1), p.66-84
• Main Authors: Phan, Raymond, Androutsos, Dimitrios
• Format: Article
• Language: English
• Subjects: Applied sciences; Artificial intelligence; Color edge detection; Color Edge Gradient Co-occurrence Histogram (CEGCH); Computer science; control theory; systems; Exact sciences and technology; HSV color quantization; Information systems. Data bases; Logo and trademark retrieval; Memory organisation. Data processing; Pattern recognition; Pattern recognition. Digital image processing. Computational geometry; Software
• ISSN: 1077-3142; 1090-235X
• DOI: 10.1016/j.cviu.2009.07.004

In this paper, we present an algorithm that extends the Color Edge Co-occurrence Histogram (CECH) object detection scheme on compound color objects, for the retrieval of logos and trademarks in unconstrained color image databases. We introduce more accurate information to the CECH, by virtue of incorporating color edge detection using vector order statistics. This produces a more accurate representation of edges in color images, as compared to the simple color pixel difference classification of edges seen with the CECH. Our proposed method is thus reliant on edge gradient information, and so we call it the Color Edge Gradient Co-occurrence Histogram (CEGCH). We also introduce a color quantization method based in the hue–saturation–value (HSV) color space, illustrating that it is a more suitable scheme of quantization for image retrieval, compared to the color quantization scheme introduced with the CECH. Experimental results demonstrate that the CEGCH and the HSV color quantization scheme is insensitive to scaling, rotation, and partial deformations, and outperforms the use of the CECH in image retrieval, with higher precision and recall. We also perform experiments on a closely related algorithm based on the Color Co-occurrence Histogram (CCH) and demonstrate that our algorithm is also superior in comparison, with higher precision and recall.