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Existing blind image quality assessment (BIQA) methods are mostly opinion-aware. They learn regression models from training images with associated human subjective scores to predict the perceptual quality of test images. Such opinion-aware methods, however, require a large amount of training samples with associated human subjective scores and of a variety of distortion types. The BIQA models learned...
Feature and metric researchings are two vital aspects in person re-identification. Metric learning seems to have gained extra advantage over feature in recent evaluations. In this paper, we explore the neglected potential of feature designing for re-identification. We propose a novel and efficient person descriptor, which is motivated by traditional spatiogram and covariance descriptors. The spatiogram...
Local Binary Pattern (LBP) has been widely used in texture classification because of its simplicity and computational efficiency. Traditional LBP codes the sign of the local difference and uses the histogram of the binary code to model the given image. However, the directional statistical information is ignored in LBP. In this paper, some directional statistical features, specifically the mean and...
A novel feature extraction method, namely monogenic binary pattern (MBP), is proposed in this paper based on the theory of monogenic signal analysis, and the histogram of MBP (HMBP) is subsequently presented for robust face representation and recognition. MBP consists of two parts: one is monogenic magnitude encoded via uniform LBP, and the other is monogenic orientation encoded as quadrant-bit codes...
In this paper, we study the problem of large scale image retrieval by developing a new class of bag-of-features to encode geometric information of objects within an image. Beyond existing orderless bag-of-features, local features of an image are first projected to different directions or points to generate a series of ordered bag-of-features, based on which different families of spatial bag-of-features...
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