Computer Vision and Image Understanding
A novel color-context descriptor and its applications
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Image retrieval based on multi-texton histogram
Pattern Recognition
Perceptual color descriptor based on spatial distribution: A top-down approach
Image and Vision Computing
A polar-based logo representation based on topological and colour features
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Image retrieval based on micro-structure descriptor
Pattern Recognition
A compact auto color correlation using binary coding stream for image retrieval
Proceedings of the 15th WSEAS international conference on Computers
Content-based image retrieval using color difference histogram
Pattern Recognition
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Information Sciences: an International Journal
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Object detection in unconstrained images is an important image understanding problem with many potential applications. There has been little success in creating a single algorithm that can detect arbitrary objects in unconstrained images; instead, algorithms typically must be customized for each specific object. Consequently, it typically requires a large number of exemplars (for rigid objects) or a large amount of human intuition (for nonrigid objects) to develop a robust algorithm. We present a robust algorithm designed to detect a class of compound color objects given a single model image. A compound color object is defined as having a set of multiple, particular colors arranged spatially in a particular way, including flags, logos, cartoon characters, people in uniforms, etc. Our approach is based on a particular type of spatial-color joint probability function called the color edge co-occurrence histogram. In addition, our algorithm employs perceptual color naming to handle color variation, and prescreening to limit the search scope (i.e., size and location) for the object. Experimental results demonstrated that the proposed algorithm is insensitive to object rotation, scaling, partial occlusion, and folding, outperforming a closely related algorithm based on color co-occurrence histograms by a decisive margin.