Color invariant object recognition using entropic graphs

We present an object recognition approach using higher‐order color invariant features with an entropy‐based similarity measure. Entropic graphs offer an unparameterized alternative to common entropy estimation techniques, such as a histogram or assuming a probability distribution. An entropic graph estimates entropy from a spanning graph structure of sample data. We extract color invariant features from object images invariant to illumination changes in intensity, viewpoint, and shading. The Henze–Penrose similarity measure is used to estimate the similarity of two images. Our method is evaluated on the ALOI collection, a large collection of object images. This object image collection consists of 1000 objects recorded under various imaging circumstances. The proposed method is shown to be effective under a wide variety of imaging conditions. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 16, 146–153, 2006

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