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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/28422661/correlated-topic-vector-for-scene-classification
#1
Pengxu Wei, Fei Qin, Fang Wan, Yi Zhu, Jianbin Jiao, Qixiang Ye
Scene images usually involve semantic correlations, particularly when considering large-scale image datasets. This paper proposes a novel generative image representation, Correlated Topic Vector, to model such semantic correlations. Oriented from the correlated topic model, Correlated Topic Vector intends to naturally utilize the correlations among topics which are seldom considered in the conventional feature encoding, e.g., Fisher Vector, but do exist in scene images. It is expected that the involvement of correlations can increase the discriminative capability of the learned generative model and consequently improve the recognition accuracy...
April 13, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28422660/weak-classifier-for-density-estimation-in-eye-localization-and-tracking
#2
Gabriel M Araujo, Felipe M L Ribeiro, Waldir S Junior, Eduardo A B da Silva, Siome K Goldenstein
In this work, we propose a fast weak classifier that can detect and track eyes in video sequences. The approach relies on a least-squares detector based on the Inner Product Detector (IPD) that can estimate a probability density distribution for a feature's location - which fits naturally with a Bayesian estimation cycle, such as a Kalman or particle filter. As a leastsquares sliding window detector, it possesses tolerance to small variations in the desired pattern while maintaining good generalization capabilities and computational efficiency...
April 13, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28422659/revisiting-co-saliency-detection-a-novel-approach-based-on-two-stage-multi-view-spectral-rotation-co-clustering
#3
Xiwen Yao, Junwei Han, Dingwen Zhang, Feiping Nie
With the goal of discovering the common and salient objects from the given image group, co-saliency detection has received tremendous research interest in recent years. However, as most of the existing co-saliency detection methods are performed based on the assumption that all the images in the given image group should contain co-salient objects in only one category, they can hardly be applied in practice, particularly for the large-scale image set obtained from the internet. To address this problem, this paper revisits the co-saliency detection task and advances its development into a new phase, where the problem setting is generalized to allow the image group to contain objects in arbitrary number of categories and the algorithms need to simultaneously detect multi-class co-salient objects from such complex data...
April 13, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28422683/progressive-dictionary-learning-with-hierarchical-predictive-structure-for-scalable-video-coding
#4
Wenrui Dai, Yangmei Shen, Hongkai Xiong, Xiaoqian Jiang, Junni Zou, David Taubman
Dictionary learning has emerged as a promising alternative to the conventional hybrid coding framework. However, the rigid structure of sequential training and prediction degrades its performance in scalable video coding. This paper proposes a progressive dictionary learning framework with hierarchical predictive structure for scalable video coding, especially in low bitrate region. For pyramidal layers, sparse representation based on spatio-temporal dictionary is adopted to improve the coding efficiency of enhancement layers (ELs) with a guarantee of reconstruction performance...
April 12, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28410109/classification-via-sparse-representation-of-steerable-wavelet-frames-on-grassmann-manifold-application-to-target-recognition-in-sar-image
#5
Ganggang Dong, Gangyao Kuang, Na Wang, Wei Wang
Automatic target recognition has been studied widely over the years, yet it is still an open problem. The main obstacle consists in extended operating conditions, e.g., depression angle change, configuration variation, articulation, occlusion. To deal with them, this paper proposes a new classification strategy. We develop a new representation model via the steerable wavelet frames. The proposed representation model is entirely viewed as an element on Grassmann manifolds. To achieve target classification, we embed Grassmann manifolds into an implicit Reproducing Kernel Hilbert Space (RKHS), where the kernel sparse learning can be applied...
April 7, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28410108/clearing-the-skies-a-deep-network-architecture-for-single-image-rain-streaks-removal
#6
Xueyang Fu, Jiabin Huang, Xinghao Ding, Yinghao Liao, John Paisley
We introduce a deep network architecture called DerainNet for removing rain streaks from an image. Based on the deep convolutional neural network (CNN), we directly learn the mapping relationship between rainy and clean image detail layers from data. Because we do not possess the ground truth corresponding to real-world rainy images, we synthesize images with rain for training. In contrast to other common strategies that increase depth or breadth of the network, we use image processing domain knowledge to modify the objective function and improve deraining with a modestly-sized CNN...
April 6, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28410107/feature-sensitive-label-fusion-with-random-walker-for-atlas-based-image-segmentation
#7
Siqi Bao, Albert C S Chung
In this paper, a novel label fusion method is proposed for brain magnetic resonance image segmentation. This label fusion method is formulated on a graph, which embraces both label priors from atlases and anatomical priors from target image. To represent a pixel in a comprehensive way, three kinds of feature vectors are generated, including intensity, gradient and structural signature. To select candidate atlas nodes for fusion, rather than exact searching, randomized k-d tree with spatial constraint is introduced as an efficient approximation for high-dimensional feature matching...
April 6, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28410106/structured-sparse-subspace-clustering-a-joint-affinity-learning-and-subspace-clustering-framework
#8
Chun-Guang Li, Chong You, Rene Vidal
Subspace clustering refers to the problem of segmenting data drawn from a union of subspaces. State-of-the-art approaches for solving this problem follow a two-stage approach. In the first step, an affinity matrix is learned from the data using sparse or low-rank minimization techniques. In the second step, the segmentation is found by applying spectral clustering to this affinity. While this approach has led to state-of-the-art results in many applications, it is sub-optimal because it does not exploit the fact that the affinity and the segmentation depend on each other...
April 6, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28410105/dynamic-textures-modeling-via-joint-video-dictionary-learning
#9
Xian Wei, Yuanxiang Li, Hao Shen, Fang Chen, Martin Kleinsteuber, Zhongfeng Wang
Video representation is an important and challenging task in the computer vision community. In this paper, we consider the problem of modeling and classifying video sequences of dynamic scenes which could be modeled in a dynamic textures (DT) framework. At first, we assume that image frames of a moving scene can be modeled as a Markov random process. We propose a sparse coding framework, named joint video dictionary learning (JVDL), to model a video adaptively. By treating the sparse coefficients of image frames over a learned dictionary as the underlying "states", we learn an efficient and robust linear transition matrix between two adjacent frames of sparse events in time series...
April 6, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28410104/low-rank-embedding-for-robust-image-feature-extraction
#10
Wai Keung Wong, Zhihui Lai, Jiajun Wen, Xiaozhao Fang, Yuwu Lu
Abstract-Robustness to noises, outliers and corruptions is an important issue in linear dimensionality reduction. Since the sample-specific corruptions and outliers exist, the class-special structure or the local geometric structure is destroyed and thus many existing methods, including the popular manifold learning based linear dimensionality methods, fail to achieve good performance in recognition tasks. In this paper, we focus on the unsupervised robust linear dimensionality reduction on corrupted data by introducing the robust low rank representation...
April 6, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28391196/energy-efficient-images
#11
Hadi Hadizadeh
In this paper, a novel method is presented for producing energy-efficient images, i.e., images that consume less electrical energy on energy-adaptive displays, yet have the same, or very similar perceptual quality to their original images. The proposed method relies on the fact the the energy consumption of pixels in modern energy-adaptive displays like OLED displays is directly proportional to the luminance of the pixels. Hence, in this paper to reduce the energy consumption of an image while at the same time preserving its perceptual quality, it is proposed to reduce the luminance of the pixels in the image by one just-noticeable-difference (JND) threshold...
April 3, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28371778/analysis-of-packet-loss-induced-distortion-in-view-synthesis-prediction-based-3-d-video-coding
#12
Pan Gao, Qiang Peng, Wei Xiang
View synthesis prediction (VSP) is a crucial coding tool for improving compression efficiency in the next generation three-dimensional (3-D) video systems. However, VSP is susceptible to catastrophic error propagation when multi-view video plus depth (MVD) data are transmitted over lossy networks. This paper aims at accurately modeling the transmission errors propagated in the inter-view direction caused by VSP. Towards this end, we first study how channel errors gradually propagate along the VSP-based inter-view prediction path...
March 30, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28371777/facial-expression-recognition-based-on-deep-evolutional-spatial-temporal-networks
#13
Kaihao Zhang, Yongzhen Huang, Yong Du, Liang Wang
One key challenging issue of facial expression recognition is to capture the dynamic variation of facial physical structure from videos. In this paper, we propose a Part-based Hierarchical Bidirectional Recurrent Neural Network (PHRNN) to analyze the facial expression information of temporal sequences. Our PHRNN models facial morphological variations and dynamical evolution of expressions, which is effective to extract "temporal features" based on facial landmarks (geometry information) from consecutive frames...
March 30, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28371776/deep-label-distribution-learning-with-label-ambiguity
#14
Bin-Bin Gao, Chao Xing, Chen-Wei Xie, Jianxin Wu, Xin Geng
Convolutional Neural Networks (ConvNets) have achieved excellent recognition performance in various visual recognition tasks. A large labeled training set is one of the most important factors for its success. However, it is difficult to collect sufficient training images with precise labels in some domains such as apparent age estimation, head pose estimation, multi-label classification and semantic segmentation. Fortunately, there is ambiguous information among labels, which makes these tasks different from traditional classification...
March 30, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28368819/selective-convolutional-descriptor-aggregation-for-fine-grained-image-retrieval
#15
Xiu-Shen Wei, Jian-Hao Luo, Jianxin Wu, Zhi-Hua Zhou
Deep convolutional neural network models pretrained for the ImageNet classification task have been successfully adopted to tasks in other domains, such as texture description and object proposal generation, but these tasks require annotations for images in the new domain. In this paper, we focus on a novel and challenging task in the pure unsupervised setting: fine-grained image retrieval. Even with image labels, fine-grained images are difficult to classify, let alone the unsupervised retrieval task. We propose the Selective Convolutional Descriptor Aggregation (SCDA) method...
March 27, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28358688/feature-selection-based-on-high-dimensional-model-representation-for-hyperspectral-images
#16
Gulsen Taskin Kaya, Huseyin Kaya, Lorenzo Bruzzone
In hyperspectral image analysis, the classification task has generally been addressed jointly with dimensionality reduction due to both the high correlation between the spectral features and the noise present in spectral bands which might significantly degrade classification performance. In supervised classification, limited training instances in proportion to the number of spectral features have negative impacts on the classification accuracy, which has known as Hughes effects or curse of dimensionality in the literature...
March 24, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28358686/scale-invariant-and-noise-robust-interest-points-with-shearlets
#17
Miguel Duval-Poo, Nicoletta Noceti, Francesca Odone, Ernesto De Vito
Shearlets are a relatively new directional multi-scale framework for signal analysis, which have been shown effective to enhance signal discontinuities such as edges and corners at multiple scales even in the presence of a large quantity of noise. In this work we consider blob-like features in the shearlets framework. We derive a measure which is very effective for blob detection and, based on this measure, we propose a blob detector and a keypoint description, whose combination outperforms the state-of-the-art algorithms with noisy and compressed images...
March 24, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28358685/robust-and-efficient-depth-reconstruction-with-hierarchical-confidence-based-matching
#18
Li Sun, Ke Chen, Mingli Song, Dacheng Tao, Gang Chen, Chun Chen
In recent years, taking photos and capturing videos with mobile devices has become increasingly popular. Emerging applications based on the depth reconstruction technique have been developed, such as Google lens blur. However, depth reconstruction is difficult due to occlusions, non-diffuse surfaces, repetitive patterns and textureless surfaces, and it has become more difficult due to the unstable image quality and uncontrolled scene condition in the mobile setting. In this paper, we present a novel hierarchical framework with multi-view confidence-based matching for robust, efficient depth reconstruction in uncontrolled scenes...
March 24, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28358684/grass-a-gradient-based-sampling-scheme-for-retinex
#19
Michela Lecca, Alessandro Rizzi, Raul P Serapioni
Retinex is an early and famous theory attempting to estimate the human color sensation derived from an observed scene. When applied to a digital image, the original implementation of Retinex estimates the color sensation by modifying the pixels channel intensities with respect to a local reference white, selected from a set of random paths. The spatial search of the local reference white influences the final estimation. The recent algorithm Energy-driven Termite Retinex (ETR), as well as its predecessor Termite Retinex, has introduced a new path-based image aware sampling scheme, where the paths depend on local visual properties of the input image...
March 23, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28333636/sequential-dictionary-learning-from-correlated-data-application-to-fmri-data-analysis
#20
Abd-Krim Seghouane, Asif Iqbal
Sequential dictionary learning via the K-SVD algorithm has been revealed as a successful alternative to conventional data driven methods such as independent component analysis (ICA) for functional magnetic resonance imaging (fMRI) data analysis. fMRI datasets are however structured data matrices with notions of spatio-temporal correlation and temporal smoothness. This prior information has not been included in the K-SVD algorithm when applied to fMRI data analysis. In this paper we propose three variants of the K-SVD algorithm dedicated to fMRI data analysis by accounting for this prior information...
March 22, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
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