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IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society

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https://www.readbyqxmd.com/read/28237930/video-based-pedestrian-re-identification-by-adaptive-spatio-temporal-appearance-model
#1
Wei Zhang, Bingpeng Ma, Kan Liu, Rui Huang
Pedestrian re-identification is a difficult problem due to the large variations in a person's appearance caused by different poses and viewpoints, illumination changes, and occlusions. Spatial alignment is commonly used to address these issues by treating the appearance of different body parts independently. However, a body part can also appear differently during different phases of an action. In this paper we consider the temporal alignment problem, in addition to the spatial one, and propose a new approach that takes the video of a walking person as input and builds a spatio-temporal appearance representation for pedestrian re-identification...
February 20, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28237929/efficient-tensor-completion-for-color-image-and-video-recovery-low-rank-tensor-train
#2
Johann A Bengua, Ho N Phiem, H D Tuan, Minh N Do
This paper proposes a novel approach to tensor completion, which recovers missing entries of data represented by tensors. The approach is based on the tensor train (TT) rank, which is able to capture hidden information from tensors thanks to its definition from a well-balanced matricization scheme. Accordingly, new optimization formulations for tensor completion are proposed as well as two new algorithms for their solution. The first one called simple low-rank tensor completion via tensor train (SiLRTC-TT) is intimately related to minimizing a nuclear norm based on TT rank...
February 20, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28237928/robust-multi-exposure-image-fusion-a-structural-patch-decomposition-approach
#3
Kede Ma, Hui Li, Hongwei Yong, Zhou Wang, Deyu Meng, Lei Zhang
We propose a simple yet effective structural patch decomposition (SPD) approach for multi-exposure image fusion (MEF) that is robust to ghosting effect. We decompose an image patch into three conceptually independent components: signal strength, signal structure, and mean intensity. Upon fusing these three components separately, we reconstruct a desired patch and place it back into the fused image. This novel patch decomposition approach benefits MEF in many aspects. First, as opposed to most pixel-wise MEF methods, the proposed algorithm does not require post-processing steps to improve visual quality or to reduce spatial artifacts...
February 20, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28237927/efficient-hardware-implementation-for-fingerprint-image-enhancement-using-anisotropic-gaussian-filter
#4
Tariq M Khan, Donald G Bailey, Mohammad A U Khan, Yinan Kong
A real-time image filtering technique is proposed which could result in faster implementation for fingerprint image enhancement. One major hurdle associated with fingerprint filtering techniques is the expensive nature of their hardware implementations. To circumvent this, a modified anisotropic Gaussian filter is efficiently adopted in hardware by decomposing the filter into two orthogonal Gaussians and an oriented line Gaussian. An architecture is developed for dynamically controlling the orientation of the line Gaussian filter...
February 20, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28237926/palette-based-image-recoloring-using-color-decomposition-optimization
#5
Qing Zhang, Chunxia Xiao, Han-Qiu Sun, Feng Tang
Previous works on palette-based color manipulation typically fail to produce visually pleasing results with vivid color and natural appearance. In this paper, we present an approach to edit colors of an image by adjusting a compact color palette. Different from existing methods that fail to preserve inherent color characteristics residing in the source image, we propose a color decomposition optimization for flexible recoloring while retaining these characteristics. For an input image, we first employ a variant of the k-means algorithm to create a palette consisting of a small set of most representative colors...
February 20, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28221996/hierarchical-latent-concept-discovery-for-video-event-detection
#6
Chao Li, Zi Huang, Yang Yang, Jiewei Cao, Xiaoshui Sun, Heng Tao Shen
Semantic information is important for video event detection. How to automatically discover, model and utilize semantic information to facilitate video event detection has been a challenging problem. In this paper, we propose a novel hierarchical video event detection model, which deliberately unifies the processes of underlying semantics discovery and event modelling from video data. Specially, different from most approaches based on manually pre-defined concepts, we devise an effective model to automatically uncover video semantics by hierarchically capturing latent static-visual concepts in frame-level and latent activity concepts (i...
February 17, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28221995/deep-cascade-cascading-3d-deep-neural-networks-for-fast-anomaly-detection-and-localization-in-crowded-scenes
#7
Mohammad Sabokrou, Mohsen Fayyaz, Mahmood Fathy, Reinhard Klette
This paper proposes a fast and reliable method for anomaly detection and localization in video data showing crowded scenes. Time-efficient anomaly localization is an ongoing challenge and subject of this paper. We propose a cubicpatch- based method, characterised by a cascade of classifiers, which makes use of an advanced feature-learning approach. Our cascade of classifiers has two main stages. First, a light but deep 3D auto-encoder is used for early identification of "many" normal cubic patches. This deep network operates on small cubic patches as being the first stage, before carefully resizing remaining candidates of interest, and evaluating those at the second stage using a more complex and deeper 3D convolutional neural network (CNN)...
February 17, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28221994/video-saliency-detection-via-spatial-temporal-fusion-and-low-rank-coherency-diffusion
#8
Chenglizhao Chen, Shuai Li, Yongguang Wang, Hong Qin, Aimin Hao
This paper advocates a novel video saliency detection method based on the spatial-temporal saliency fusion and low-rank coherency guided saliency diffusion. In sharp contrast to the conventional methods, which conduct saliency detection locally in a frame-by-frame way and could easily give rise to incorrect low-level saliency map, in order to overcome the existing difficulties, this paper proposes to fuse the color saliency based on global motion clues in a batch-wise fashion. And we also propose low-rank coherency guided spatial-temporal saliency diffusion to guarantee the temporal smoothness of saliency maps...
February 16, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28212087/dual-deep-network-for-visual-tracking
#9
Zhizhen Chi, Hongyang Li, Huchuan Lu, Minghsuan Yang
Visual tracking addresses the problem of identifying and localizing an unknown target in a video given the target specified by a bounding box in the first frame. In this paper, we propose a dual network to better utilize features among layers for visual tracking. It is observed that features in higher layers encode semantic context while its counterparts in lower layers are sensitive to discriminative appearance. Thus we exploit the hierarchical features in different layers of a deep model and design a dual structure to obtain better feature representation from various streams, which is rarely investigated in previous work...
February 15, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28212086/salient-object-detection-via-multiple-instance-learning
#10
Fang Huang, Qi Jinqing, Huchuan Lu, Lihe Zhang, Xiang Ruan
Object proposals are a series of candidate segments containing objects of interest, which are taken as preprocessing and widely applied in various vision tasks. However, most of existing saliency approaches only utilize the proposals to compute a location prior. In this paper, we naturally take the proposals as the bags of instances of multiple instance learning (MIL), where the instances are the superpixels contained in the proposals, and formulate saliency detection problem as a MIL task (i.e., predict the labels of instances using the classifier in the MIL framework)...
February 15, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28212085/contextual-noise-reduction-for-domain-adaptive-near-duplicate-retrieval-on-merchandize-images
#11
Zhen-Qun Yang, Xiao-Yong Wei, Zhang Yi, Gerald Friedland
In this paper, we have proposed a novel method which utilizes the contextual relationship among visual words for reducing the Quantization errors in near-duplicate image retrieval (NDR). Instead of following the track of conventional NDR techniques which usually search new solutions by borrowing ideas from the text domain, we propose to model the problem back to image domain, which results in a more natural way of solution search. The idea of the proposed method is to construct a context graph that encapsulates the contextual relationship within an image and treat the graph as a pseudo-image, so that classical image filters can be adopted to reduce the mismapped visual words which are contextually inconsistent with others...
February 15, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28212084/objective-quality-assessment-of-screen-content-images-by-uncertainty-weighting
#12
Yuming Fang, Jiebin Yan, Jiaying Liu, Shiqi Wang, Qiaohong Li, Zongming Guo
In this paper, we propose a novel full-reference objective quality assessment metric for screen content images (SCIs) by structure features and uncertainty weighting (SFUW). The input SCI is first divided into textual and pictorial regions. The visual quality of textual regions is estimated based on perceptual structural similarity, where the gradient information is adopted as the structural feature. To predict the visual quality of pictorial regions in SCIs, we extract the structural features and luminance features for similarity computation between the reference and distorted pictorial patches...
February 15, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28212083/aliasing-detection-and-reduction-scheme-on-angularly-undersampled-light-fields
#13
Zhaolin Xiao, Qing Wang, Guoqing Zhou, Jingyi Yu
When using plenoptic camera for digital refocusing, angular undersampling can cause severe (angular) aliasing artifacts. Previous approaches have focused on avoiding aliasing by pre-processing the acquired light field via prefiltering, demosaicing, reparameterization, etc. In this paper, we present a different solution that first detects and then removes angular aliasing at the light field refocusing stage. Different from previous frequency domain aliasing analysis, we carry out a spatial domain analysis to reveal whether the angular aliasing would occur and uncover where in the image it would occur...
February 13, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28212082/semantic-highlight-retrieval-and-term-prediction
#14
Min Sun, Kuo-Hao Zeng, Yenchen Lin, Farhadi Ali
Due to the unprecedented growth of unedited videos, finding highlights relevant to a text query in a set of unedited videos has become increasingly important. We refer this task as semantic highlight retrieval and propose a query-dependent video representation for retrieving a variety of highlights. Our method consists of two parts: (1) "viralets", a mid-level representation bridging between semantic (Fig. 1(a)) and visual (Fig. 1(c)) spaces; (2) a novel Semantic-MODulation (SMOD) procedure to make viralets query-dependent (referred to as SMOD viralets)...
February 13, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28207397/stacked-learning-to-search-for-scene-labeling
#15
Feiyang Cheng, Xuming He, Hong Zhang
Search-based structured prediction methods have shown promising successes in both computer vision and natural language processing recently. However, most existing search-based approaches lead to a complex multi-stage learning process, which is ill-suited for scene labeling problems with a high-dimensional output space. In this paper, a stacked learning to search method is proposed to address scene labeling tasks. We design a simplified search process consisting of a sequence of ranking functions, which are learned based on a stacked learning strategy to prevent over-fitting...
February 13, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28207396/hd-mtl-hierarchical-deep-multi-task-learning-for-large-scale-visual-recognition
#16
Jianping Fan, Tianyi Zhao, Zhenzhong Kuang, Yu Zheng, Ji Zhang, Jun Yu, Jinye Peng
In this paper, a hierarchical deep multi-task learning (HD-MTL) algorithm is developed to support large-scale visual recognition (e.g., recognizing thousands or even tens of thousands of atomic object classes automatically). First, multiple sets of multi-level deep features are extracted from different layers of deep convolutional neural networks (deep CNNs), and they are used to achieve more effective accomplishment of the coarseto- fine tasks for hierarchical visual recognition. A visual tree is then learned by assigning the visually-similar atomic object classes with similar learning complexities into the same group, which can provide a good environment for determining the interrelated learning tasks automatically...
February 9, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28207395/video-vectorization-via-tetrahedral-remeshing
#17
Chuan Wang, Jie Zhu, Yanwen Guo, Wenping Wang
We present a video vectorization method that generates a video in vector representation from an input video in raster representation. A vector-based video representation offers the benefits of vector graphics, such as compactness and scalability. The vector video we generate is represented by a simplified tetrahedral control mesh over the spatial-temporal video volume, with color attributes defined at the mesh vertices. We present novel techniques for simplification and subdivision of a tetrahedral mesh to achieve high simplification ratio while preserving features and ensuring color fidelity...
February 9, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28207394/weakly-supervised-patchnets-describing-and-aggregating-local-patches-for-scene-recognition
#18
Zhe Wang, Limin Wang, Yali Wang, Bowen Zhang, Yu Qiao
Traditional feature encoding scheme (e.g., Fisher vector) with local descriptors (e.g., SIFT) and recent convolutional neural networks (CNNs) are two classes of successful methods for image recognition. In this paper, we propose a hybrid representation, which leverages the discriminative capacity of CNNs and the simplicity of descriptor encoding schema for image recognition, with a focus on scene recognition. To this end, we make three main contributions from the following aspects. First, we propose a patch-level and end-to-end architecture to model the appearance of local patches, called PatchNet...
February 9, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28186897/weighted-level-set-evolution-based-on-local-edge-features-for-medical-image-segmentation
#19
Alaa Khadidos, Victor Sanchez, Chang-Tsun Li
Level set methods have been widely used to implement active contours for image segmentation applications due to their good boundary detection accuracy. In the context of medical image segmentation, weak edges and inhomogeneities remain important issues that may hinder the accuracy of any segmentation method based on active contours implemented using level set methods. This paper proposes a method based on active contours implemented using level set methods for segmentation of such medical images. The proposed method uses a level set evolution that is based on the minimization of an objective energy functional whose energy terms are weighted according to their relative importance in detecting boundaries...
February 8, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28186896/robust-and-dense-depth-estimation-for-light-field-images
#20
Julia Navarro, Antoni Buades
We propose a depth estimation method for light field images. Light field images can be considered as a collection of 2D images taken from different viewpoints arranged in a regular grid. We exploit this configuration and compute the disparity maps between specific pairs of views. This computation is carried out by a state of the art two-view stereo method providing a non dense disparity estimation. We propose a disparity interpolation method increasing the density and improving the accuracy of this initial estimate...
February 8, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
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