Image category recognition is important to access visual information on the level of objects and scene types. In this paper, we propose a new approach for color object recognition using the powerful information provided by the color. This approach is based on the combination of Gray-Edge color constancy, hue components in HSV (hue, saturation, value) color space and cell and bin ideas used in the HOG (Histograms of Gradients) descriptors. The proposed oriented descriptor benefits of the invariance of hues against light intensity change, light intensity shift and light intensity change and shift, and solve its missing of invariance against light color change by using Gray-Edge color constancy. Moreover, the use of cells and bins in this proposed descriptor building boost its invariance the geometric and photo-metric transformation and increases the recognition rate. SVM classifiers (Support Vector Machine) which is a strong classification method known for its flexibility and its power of generalization are used for the training and recognition steps. The proposed method is evaluated on two publicly available datasets including Columbia Object Image Library and The Amsterdam Library of Object Images and obtained a recognition rate of 95.64% and 96.48% - clearly showing the exceptional performance compared to existing methods.

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In the last few years, there has been a lot of interest in making smart components, e.g. robots, able to simulate human capacity of object recognition and categorization. In this paper, we propose a new revolutionary approach for object categorization based on combining the HOG (Histograms of Oriented Gradients) descriptors with our two new descriptors, HOH (Histograms of Oriented Hue) and HOS (Histograms of Oriented Saturation), designed it in the HSL (Hue, Saturation and Luminance) color space and inspired by this famous HOG descriptor. By using the chrominance components, we have succeeded in making the proposed descriptor invariant to all lighting conditions changes. Moreover, the use of this oriented gradient makes our descriptor invariant to geometric condition changes including geometric and photometric transformation. Finally, the combination of color and gradient information increase the recognition rate of this descriptor and give it an exceptional performance compared to existing methods in the recognition of colored handmade objects with uniform background (98.92% for Columbia Object Image Library and 99.16% for the Amsterdam Library of Object Images). For the classification task, we propose the use of two strong and very used classifiers, SVM (Support Vector Machine) and KNN (k-nearest neighbors) classifiers.