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Despite recent attempts for solving the person re-identification problem, it remains a challenging task since a person’s appearance can vary significantly when large variations in view angle, human pose and illumination are involved. The concept of attention is one of the most interesting recent architectural innovations in neural networks. Inspired by that, in this paper we propose a novel approach based on using a gradient-based attention mechanism in deep convolution neural network for solving the person re-identification problem.

Binary hashing is a practical approach for fast, approximate retrieval in large image databases. The goal is to learn a hash function that maps images onto a binary vector such that Hamming distances approximate semantic similarities. The search is then fast by using hardware support for binary operations. Most hashing papers define a complicated objective function that couples the single-bit hash functions.

Convolutional neural networks are top-performers on image
classification tasks. Understanding how they make use of
color information in images may be useful for various tasks.
In this paper we analyze the representation learned by a popular
CNN to detect and characterize color-related features.
We confirm the existence of some object- and color-specific
units, as well as the effect of layer-depth on color-sensitivity
and class-invariance.

Here we present improvements to a dynamic texture synthesis approach which is based on motion distribution statistics, able to produce high visual quality of synthesised dynamic textures. The aim is to recreate synthetically highly textured regions like water, leaves and smoke, instead of processing them with a conventional codec such as HEVC. The method involves two steps: analysis, where motion distribution statistics are computed, and synthesis, where the texture region is synthesized. Dense optical flow is utilized for estimating the random motion of dynamic textures.

We introduce a novel approach to improve unsupervised hashing. Specifically, we propose a very efficient embedding method: Gaussian Mixture Model embedding (Gemb). The proposed method, using Gaussian Mixture Model, embeds feature vector into a low-dimensional vector and, simultaneously, enhances the discriminative property of features before passing them into hashing. Our experiment shows that the proposed method boosts the hashing performance of many state-of-the-art, e.g.

We introduce a novel approach to improve unsupervised hashing. Specifically, we propose a very efficient embedding method: Gaussian Mixture Model embedding (Gemb). The proposed method, using Gaussian Mixture Model, embeds feature vector into a low-dimensional vector and, simultaneously, enhances the discriminative property of features before passing them into hashing. Our experiment shows that the proposed method boosts the hashing performance of many state-of-the-art, e.g.