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This work proposes a novel person re-identification method based on Hierarchical Bipartite Graph Matching. Because human eyes observe person appearance roughly first and then goes further into the details gradually, our method abstracts person image from coarse to fine granularity, and finally into a three layer tree structure. Then, three bipartite graph matching methods are proposed for the matching...
This paper presents a method for dataset manipulation based on Mixed Integer Linear Programming (MILP). The proposed optimization can narrow down a dataset to a particular size, while enforcing specific distributions across different dimensions. It essentially leverages the redundancies of an initial dataset in order to generate more compact versions of it, with a specific target distribution across...
The main purpose of transfer learning is to resolve the problem of different data distribution, generally, when the training samples of source domain are different from the training samples of the target domain. Prediction of salient areas in natural video suffers from the lack of large video benchmarks with human gaze fixations. Different databases only provide dozens up to one or two hundred of...
We consider the use of transfer learning, via the use of deep Convolutional Neural Networks (CNN) for the image classification problem posed within the context of X-ray baggage security screening. The use of a deep multi-layer CNN approach, traditionally requires large amounts of training data, in order to facilitate construction of a complex complete end-to-end feature extraction, representation...
Distributed object recognition is a significantly fast-growing research area, mainly motivated by the emergence of high performance cameras and their integration with modern wireless sensor network technologies. In wireless distributed object recognition, the bandwidth is limited and it is desirable to avoid transmitting redundant visual features from multiple cameras to the base station. In this...
Density estimation based visual object counting (DE-VOC) methods estimate the counts of an image by integrating over its predicted density map. They perform effectively but inefficiently. This paper proposes a fast DE-VOC method but maintains its effectiveness. Essentially, the feature space of image patches from VOC can be clustered into subspaces, and the examples of each subspace can be collected...
In this paper, we present a novel self-learning single image super-resolution (SR) method, which restores a highresolution (HR) image from self-examples extracted from the low-resolution (LR) input image itself without relying on extra external training images. In the proposed method, we directly use sampled image patches as the anchor points, and then learn multiple linear mapping functions based...
Resolution in medical images is limited by diverse physical, technological and economical considerations. In conventional medical practice, resolution enhancement is usually performed with bicubic or B-spline interpolations, strongly affecting the accuracy of subsequent processing steps such as segmentation or registration. In this paper, we propose a coupled dictionary learning approach for super...
The objective of this paper is the fully automated visual identification of individual Holstein Friesian cattle from dorsal RGB-D imagery taken in real-world farm environments. Autonomous and non-intrusive cattle identification could provide an essential tool for economically-viable machinised farming analytics, social monitoring, cattle traceability, food production management and more. We contribute...
“Ceci n'est pas une pipe” French for “This is not a pipe”. This is the description painted on the first painting in the figure above. But to most of us, how could this painting is not a pipe, at least not to the great Belgian surrealist artist Rene Magritte. He said that the painting is not a pipe, but rather an image of a pipe. In this paper, we present a study on large-scale classification of fine-art...
Visual question answering (VQA) comes as a result of great development in computer vision and natural language processing, which requires deep understanding of images and questions and effective integration of them. Current works on VQA simply concatenated visual and textual features or compared them via dot product, which were unable to eliminate the semantic difference between them. We argue to...
Selecting local features is crucial in generating robust compact descriptors for mobile visual search. The state-of-the-art MPEG Compact Descriptors for Visual Search (CDVS) standard has utilized the intrinsic characteristics (e.g., scale, orientation, peak, center distance, etc.) of interest points to select salient local features for selective aggregation and compression of local feature descriptors...
This paper presents a novel method of fixation identification for mobile eye trackers. The most significant benefit of our method over the state-of-the-art is that it achieves high accuracy for low-sample-rate devices worn during locomotion. This in turn delivers higher quality datasets for further use in human behaviour research, robotics and the development of guidance aids for the visually impaired...
Curators, art historians, and connoisseurs are often interested in determining the authorship of paintings. Machine learning and image processing techniques can assist in this task by providing non-invasive, automatic, and objective methods. In this work, we study the automatic identification of Vincent van Gogh's paintings using a Convolutional Neural Network that extracts discriminative visual patterns...
In this study, we address the problem of infrared (IR) object classification that divides the object appearance space hierarchically with a binary decision tree structure. Binary decisions are made by using the special features of the object appearances. These features are extracted using a fully connected deep neural network learnt by training samples. At each node of the tree, we train individual...
Zero shot learning (ZSL) provides a solution to recognising unseen classes without class labelled data for model learning. Most ZSL methods aim to learn a mapping from a visual feature space to a semantic embedding space, e.g. attribute or word vector spaces. The use of word vector space is particularly attractive as compared to attribute, it offers vast auxiliary classes with free parts embedding...
Multiple Instance Learning (MIL) recently provides an appealing way to alleviate the drifting problem in visual tracking. Following the tracking-by-detection framework, an online MILBoost approach is developed that sequentially chooses weak classifiers by maximizing the bag likelihood. In this paper, we extend this idea towards incorporating the instance significance estimation into the online MILBoost...
Robust scale and rotation estimation is an important and challenging problem in visual object tracking. There have been proposed many sophisticated trackers to track the location of a target accurately, but most of them do not take much attention to the scale and rotation estimation. Inspired by the success of the correlation filters in visual tracking, we proposed a novel scale-and-rotation correlation...
This paper presents a novel method of salience and priority estimation for the human visual system during locomotion. This visual information contains dynamic content derived from a moving viewpoint. The priority map, ranking key areas on the image, is created from probabilities of gaze fixations, merged from bottom-up features and top-down control on the locomotion. Two deep convolutional neural...
Emotional factors usually affect users' preferences for and evaluations of images. Although affective image analysis attracts increasing attention, there are still three major challenges remaining: 1) it is difficult to classify an image into a single emotion type since different regions within an image can represent different emotions; 2) there is a gap between low-level features and high-level emotions...
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