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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/28809686/salient-region-detection-using-diffusion-process-on-a-2-layer-sparse-graph
#1
Li Zhou, Zhaohui Yang, Zongtan Zhou, Dewen Hu
Diffusion-based salient region detection has recently received intense research attention. In this paper, we present some effective improvements concerning two important aspects of diffusion-based methods: the construction of the diffusion matrix and the seed vector. First, we construct a 2-layer sparse graph, which is generated by connecting each node to its neighboring nodes and the most similar node that shares common boundaries with its neighboring nodes. Compared with the most frequently used 2-layer neighborhood graph, our graph not only effectively uses local spatial relationships, but also removes dissimilar redundant nodes...
August 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28809685/human-motion-segmentation-via-robust-kernel-sparse-subspace-clustering
#2
Guiyu Xia, Huaijiang Sun, Lei Feng, Guoqing Zhang, Yazhou Liu
Studies on human motion have attracted a lot of attentions. Human motion capture data, which much more precisely records human motion than videos do, has been widely used in many areas. Motion segmentation is an indispensable step for many related applications, but current segmentation methods for motion capture data do not effectively model some important characteristics of motion capture data, such as Riemannian manifold structure and containing non-Gaussian noise. In this paper, we convert the segmentation of motion capture data into a temporal subspace clustering problem...
August 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28809684/combination-of-sharing-matrix-and-image-encryption-for-lossless-k-n-secret-image-sharing
#3
Long Bao, Shuang Yi, Yicong Zhou
This paper first introduces a (k, n)-sharing matrix S(k,n) and its generation algorithm. Mathematical analysis is provided to show its potential for secret image sharing. Combining sharing matrix with image encryption, we further propose a lossless (k, n)-secret image sharing scheme (SMIE-SIS). The chaotic image encryption is extremely sensitive to ciphertext. Little change in the ciphertext will lead to failure of noise-like decrypted results even with right key. Thus, even though sharing matrix will recover part of ciphertext information when less than k secret shares are combined, its missing information will result in a wrong key and a noise-like decryption result, keeping the secret from leakage...
August 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28809683/low-rank-latent-pattern-approximation-with-applications-to-robust-image-classification
#4
Shuo Chen, Jian Yang, Lei Luo, Yang Wei, Kaihua Zhang, Ying Tai
This paper develops a novel method to address the structural noise in samples for image classification. Recently, regression related classification methods have shown promising results when facing the pixel-wise noise. However, they become weak in coping with the structural noise due to ignoring of relationships between pixels of noise image. Meanwhile, most of them need to implement the iterative process for computing representation coefficients, which leads to the high time consumption. To overcome these problems, we exploit a latent pattern model called Low-Rank Latent Pattern Approximation (LLPA) to reconstruct the test image having structural noise...
August 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28796619/unsupervised-t-distributed-video-hashing-and-its-deep-hashing-extension
#5
Yanbin Hao, Tingting Mu, John Y Goulermas, Jianguo Jiang, Richang Hong, Meng Wang
In this work, a novel unsupervised hashing algorithm, referred to as t-USMVH, and its extension to unsupervised deep hashing, referred to as t-UDH, are proposed to support large-scale video-to-video retrieval. To improve robustness of the unsupervised learning, t-USMVH combines multiple types of feature representations and effectively fuses them by examining a continuous relevance score based on a Gaussian estimation over pairwise distances, and also a discrete neighbor score based on the cardinality of reciprocal neighbors...
August 7, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28796618/learning-based-shadow-recognition-and-removal-from-monochromatic-natural-images
#6
Mingliang Xu, Jiejie Zhu, Pei Lv, Bing Zhou, Marshall F Tappen, Rongrong Ji
This paper addresses the problem of recognizing and removing shadows from monochromatic natural images from a learning based perspective. Without chromatic information, shadow recognition and removal are extremely challenging in the literature, mainly due to the missing of invariant color cues. Natural scenes make this problem even harder due to the complex illumination condition and ambiguity from many near-black objects. In this paper, a learning based shadow recognition and removal scheme is proposed to tackle the challenges above...
August 7, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28792900/dynamic-video-stitching-via-shakiness-removing
#7
Yongwei Nie, Tan Su, Zhensong Zhang, Hanqiu Sun, Guiqing Li
Stitching videos captured by hand-held mobile cameras can essentially enhance entertainment experience of ordinary users. However, such videos usually contain heavy shakiness and large parallax, which are challenging to stitch. In this paper, we propose a novel approach of video stitching and stabilization for videos captured by mobile devices. The main component of our method is a unified video stitching and stabilization optimization that computes stitching and stabilization simultaneously rather than does each one individually...
August 7, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28792899/full-reference-quality-assessment-for-image-retargeting-based-on-natural-scene-statistics-modeling-and-bi-directional-saliency-similarity
#8
Zhibo Chen, Jianxin Lin, Ning Liao, Chang Wen Chen
Image retargeting technology has been widely studied to adapt images for the devices with heterogeneous screen resolutions. Meanwhile effective objective retargeting quality assessment algorithms are also very important for optimizing and selecting favorable retargeting methods. Unlike previous assessment algorithms which rely on image local structure features and unidirectional prediction of information loss, we propose a Bi-directional Natural Salient Scene Distortion model (BNSSD) including image Natural Scene Statistics (NSS) measurement, salient global structure distortion measurement and bi-directional salient information loss measurement...
August 4, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28792898/least-squares-image-estimation-for-large-data-in-the-presence-of-noise-and-irregular-sampling
#9
Lorenzo Piazzo
We consider an acquisition system where a continuous, band-limited image is reconstructed from a set of irregularly distributed, noisy samples. An optimal estimator can be obtained by exploiting Least Squares (LS), but it is not practical to compute when the data size is large. A simpler, widely used estimate can be obtained by properly rounding off the pointing information, but it is suboptimal and is affected by a bias which may be large and thus limits its applicability. To solve this problem, we develop a mathematical model for the acquisition system which accounts for the pointing information round off...
August 4, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28792897/partial-membership-latent-dirichlet-allocation-for-soft-image-segmentation
#10
Chao Chen, Alina Zare, Huy N Trinh, Gbenga O Omotara, J Tory Cobb, Timotius A Lagaunne
Topic models (e.g., pLSA, LDA, sLDA) have been widely used for segmenting imagery. However, these models are confined to crisp segmentation, forcing a visual word (i.e., an image patch) to belong to one and only one topic. Yet, there are many images in which some regions cannot be assigned a crisp categorical label (e.g., transition regions between a foggy sky and the ground or between sand and water at a beach). In these cases, a visual word is best represented with partial memberships across multiple topics...
August 4, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28792896/unsupervised-primary-object-discovery-in-videos-based-on-evolutionary-primary-object-modeling-with-reliable-object-proposals
#11
Yeong Jun Koh, Chang-Su Kim
A novel primary object discovery (POD) algorithm, which uses reliable object proposals while exploiting the recurrence property of a primary object in a video sequence, is proposed in this work. First, we generate both color-based and motion-based object proposals in each frame, and extract the feature of each proposal using the random walk with restart simulation. Next, we estimate the foreground confidence for each proposal to remove unreliable proposals. By superposing the features of the remaining reliable proposals, we construct the primary object models...
August 4, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28792895/cross-domain-shoe-retrieval-with-a-semantic-hierarchy-of-attribute-classification-network
#12
Huijing Zhan, Boxin Shi, Alex C Kot
Cross-domain shoe image retrieval is a challenging problem, because the query photo from the street domain (daily life scenario) and the reference photo in the online domain (online shop images) have significant visual differences due to the viewpoint and scale variation, self-occlusion, and cluttered background. This paper proposes the Semantic Hierarchy Of attributE Convolutional Neural Network (SHOE-CNN) with a three-level feature representation for discriminative shoe feature expression and efficient retrieval...
August 4, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28783636/unified-blind-quality-assessment-of-compressed-natural-graphic-and-screen-content-images
#13
Xiongkuo Min, Kede Ma, Ke Gu, Guangtao Zhai, Zhou Wang, Weisi Lin
Digital images in the real-world are created by a variety of means and have diverse properties. A photographical natural scene image (NSI) may exhibit substantially different characteristics from a computer graphic image (CGI) or a screen content image (SCI). This casts major challenges to objective image quality assessment, for which existing approaches lack effective mechanisms to capture such content type variations, and thus are difficult to generalize from one type to another. To tackle this problem, we first construct a cross-content-type (CCT) database which contains 1,320 distorted NSIs, CGIs, and SCIs, compressed using the high efficiency video coding (HEVC) intra coding method and the screen content compression (SCC) extension of HEVC...
August 2, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28783635/out-of-sample-extension-for-dimensionality-reduction-of-noisy-time-series
#14
Hamid Dadkhahi, Marco F Duarte, Benjamin M Marlin
This paper proposes an out-of-sample extension framework for a global manifold learning algorithm (Isomap) that uses temporal information in out-of-sample points in order to make the embedding more robust to noise and artifacts. Given a set of noise-free training data and its embedding, the proposed framework extends the embedding for a noisy time series. This is achieved by adding a spatiotemporal compactness term to the optimization objective of the embedding. To the best of our knowledge, this is the first method for out-of-sample extension of manifold embeddings that leverages timing information available for the extension set...
August 2, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28783634/nonnegative-decompositions-for-dynamic-visual-data-analysis
#15
Lazaros Zafeiriou, Yannis Panagakis, Maja Pantic, Stefanos Zafeiriou
The analysis of high-dimensional, possibly temporally misaligned, time-varying visual data is a fundamental task in disciplines such as image, vision, and behaviour computing. In this paper, we focus on dynamic facial behaviour analysis and in particular on the analysis of facial expressions. Distinct from the previous approaches, where sets of facial landmarks are used for face representation, raw pixel intensities are exploited for 1) unsupervised analysis of the temporal phases of facial expressions and facial action unitis (AUs) and 2) temporal alignment of a certain facial behaviour displayed by two different persons...
August 2, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28783633/towards-optimal-manifold-hashing-via-discrete-locally-linear-embedding
#16
Rongrong Ji, Hong Liu, Liujuan Cao, Di Liu, Yongjian Wu, Feiyue Huang
Binary code learning, a.k.a. hashing, has received increasing attention in large-scale visual search. By transforming high-dimensional features to binary codes, the original Euclidean distance is approximated via Hamming distance. More recently, it is advocated that it is the manifold distance, rather than the Euclidean distance, that should be preserved in the Hamming space. However, it retains as an open problem to directly preserve the manifold structure by hashing. In particular, it needs first to build the local linear embedding in the original feature space, and then quantize such embedding to binary codes...
August 2, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28783632/deepskeleton-learning-multi-task-scale-associated-deep-side-outputs-for-object-skeleton-extraction-in-natural-images
#17
Wei Shen, Kai Zhao, Yuan Jiang, Yan Wang, Xiang Bai, Alan Yuille
Object skeletons are useful for object representation and object detection. They are complementary to the object contour, and provide extra information, such as how object scale (thickness) varies among object parts. But object skeleton extraction from natural images is very challenging, because it requires the extractor to be able to capture both local and non-local image context in order to determine the scale of each skeleton pixel. In this paper, we present a novel fully convolutional network with multiple scale-associated side outputs to address this problem...
August 2, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28783631/wide-baseline-foreground-object-interpolation-using-silhouette-shape-prior
#18
Cedric Verleysen, Thomas Maugey, Pascal Frossard, Christophe De Vleeschouwer
We consider the synthesis of intermediate views of an object captured by two widely spaced and calibrated cameras. This problem is challenging because foreshortening effects and occlusions induce significant differences between the reference images when the cameras are far apart. That makes the association or disappearance/appearance of their pixels difficult to estimate. Our main contribution lies in disambiguating this illposed problem by making the interpolated views consistent with a plausible transformation of the object silhouette between the reference views...
July 31, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28767371/category-specific-object-image-denoising
#19
Saeed Anwar, Fatih Porikli, Cong Phuoc Huynh
We present a novel image denoising algorithm that uses external, category specific image database. In contrast to existing noisy image restoration algorithms that search patches either from a generic database or noisy image itself, our method first selects clean images similar to the noisy image from a database that consists of images of the same class. Then, within the spatial locality of each noisy patch, it assembles a set of "support patches" from the selected images. These noisyfree support samples resemble the noisy patch and correspond principally to the identical part of the depicted object...
July 31, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28767370/lle-score-a-new-filter-based-unsupervised-feature-selection-method-based-on-nonlinear-manifold-embedding-and-its-application-to-image-recognition
#20
Chao Yao, Ya-Feng Liu, Bo Jiang, Jungong Han, Junwei Han
The task of feature selection is to find the most representative features from the original high-dimensional data. Because of the absence of the information of class labels, selecting the appropriate features in unsupervised learning scenarios is much harder than that in supervised scenarios. In this paper, we investigate the potential of Locally Linear Embedding (LLE), which is a popular manifold learning method, in feature selection task. It is straightforward to apply the idea of LLE to the graphpreserving feature selection framework...
July 28, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
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