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To localize objects in Web images using an invariant descriptor is crucial. The HOG (histogram of oriented gradients) descriptor is used to increase the accuracy of localization. It is a shape descriptor that considers frequencies of gradient orientation in localized portions of an image. This well known descriptor does not cover rotation variations of an object in images. This paper introduces a rotation invariant feature descriptor based on HOG. The proposed descriptor is used in a top-down searching technique that covers the scale variation of the objects in images. The efficiency of this method is validated by comparing the performance with existing research in a similar domain on the Caltech-256 Web dataset. The proposed method not only provides robustness against geometrical transformations of objects but also is computationally more efficient. HighlightsGeometrical invariant Object localization/recognition in Web images.The Rotation Invariant HOG (RIHOG).Fast scale invariant object localization/recognition in images.The Rapid Ranking Method (RRM) is based on Top-Down searching under RIHOG.Geometrical invariant image ranking method.

Human recognition is a practical field in Computer Vision and lot of research has been done on this field. In this paper we analyzed human recognition with new method which based on features that inspired from biological vision system. Here is shown that this method has good performance on human recognition and could be counted on as a reliable method in this field. Besides, it proposed a change in this method to have good trade-off between elapsed time to learn features and its performance to recognize a human in digital images.