Abstract Image classifification methods have been signifificantly developed in the last decade. Most methods stem from bagof-features (BoF) approach and it is recently extended to a vector aggregation model, such as using Fisher kernels. In this paper, we propose a novel feature extraction method for image classifification. Following the BoF approach, a plenty of local descriptors are fifirst extracted in an image and the proposed method is built upon the probability density function (p.d.f) formed by those descriptors. Since the p.d.f essentially represents the image, we extract the features from the p.d.f by means of the gradients on the p.d.f. The gradients, especially their orientations, effectively characterize the shape of the p.d.f from the geometrical viewpoint. We construct the features by the histogram of the oriented p.d.f gradients via orientation coding followed by aggregation of the orientation codes. The proposed image features, imposing no specifific assumption on the targets, are so general as to be applicable to any kinds of tasks regarding image classififications. In the experiments on object recognition and scene classifification using various datasets, the proposed method exhibits superior performances compared to the other existing methods.