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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/30010578/spontaneous-expression-recognition-using-universal-attribute-model
#1
Nazil Perveen, Debaditya Roy, C Krishna Mohan
Spontaneous expression recognition refers to recognizing non-posed human expressions. In literature, most of the existing approaches for expression recognition mainly rely on manual annotations by experts, which is both time-consuming and difficult to obtain. Hence, we propose an unsupervised framework for spontaneous expression recognition that preserves discriminative information for the videos of each expression without using annotations. Initially, a large Gaussian mixture model called universal attribute model (UAM) is trained to learn the attributes of various expressions implicitly...
July 16, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30010577/image-stacks-as-parametric-surfaces-application-to-image-registration
#2
Birmingham Hang Guan, Anand Rangarajan
We introduce a framework in which a stack of images is considered to be a 2-dimensional parametric surface embedded in a higher dimensional space. This is a simple yet powerful idea, known in the literature but not exploited to its fullest. We apply this framework to image registration, discuss the properties of image stacks as parametric surfaces (ISPS), and present the image stack surface relative area (ISSRA) registration measure. We show the power of ISSRA as an effective objective function for image registration...
July 13, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30010576/quality-robust-mixtures-of-deep-neural-networks
#3
Samuel Dodge, Lina Karam
We study deep neural networks for classification of images with quality distortions. Deep network performance on poor quality images can be greatly improved if the network is fine-tuned with distorted data. However, it is difficult for a single fine-tuned network to perform well across multiple distortion types. We propose a mixture of experts based ensemble method, MixQualNet, that is robust to multiple different types of distortions. The "experts" in our model are trained on a particular type of distortion...
July 13, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30010575/recognition-from-web-data-a-progressive-filtering-approach
#4
Jufeng Yang, Xiaoxiao Sun, Yu-Kun Lai, Liang Zheng, Ming-Ming Cheng
Leveraging the abundant number of web data is a promising strategy in addressing the problem of data lacking when training convolutional neural networks (CNNs). However, web images often contain incorrect tags, which may compromise the learned CNN model. To address this problem, this paper focuses on image classification and proposes to iterate between filtering out noisy web labels and fine-tuning the CNN model using the crawled web images. Overall, the proposed method benefits from the growing modeling capability of the learned model to correct labels for web images and learning from such new data to produce a more effective model...
July 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30010574/exemplar-aided-salient-object-detection-via-joint-latent-space-embedding
#5
Yuqiu Kong, Jianming Zhang, Huchuan Lu, Xiuping Liu
Traditional unsupervised salient object detection methods majorly rely on pre-defined assumptions about saliency. However, these assumptions may not be sufficient for handling test images of varied content and context. Meanwhile, supervised models learn saliency knowledge from thousands of annotated images, which are usually expensive to obtain. In this paper, we propose an exemplar-aided salient object detection method, which can complement heuristic saliency assumptions by leveraging only a few exemplar images...
July 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30010573/information-fusion-for-human-action-recognition-via-biset-multiset-globality-locality-preserving-canonical-correlation-analysis
#6
Nour El Din Elmadany, Yifeng He, Ling Guan
In this paper, we study the problem of human action recognition, in which each action is captured by multiple sensors and represented by multisets. We propose two novel information fusion techniques for fusing the information from multisets. The first technique is Biset Globality Locality Preserving Canonical Correlation Analysis (BGLPCCA), which aims to learn the common feature subspace between two sets. The second technique is Multiset Globality Locality Preserving Canonical Correlation Analysis (MGLPCCA), which aims to deal with three or more sets...
July 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30010572/structurally-incoherent-low-rank-nonnegative-matrix-factorization-for-image-classification
#7
Yuwu Lu, Chun Yuan, Wenwu Zhu, Xuelong Li
As a popular dimensionality reduction method, nonnegative matrix factorization (NMF) has been widely used in image classification. However, NMF does not consider discriminant information from the data themselves. In addition, most NMF-based methods use the Euclidean distance as a metric, which is sensitive to noise or outliers in data. To solve these problems, in this paper, we introduce structural incoherence and low-rank to NMF and propose a novel nonnegative factorization method, called structurally incoherent low-rank NMF (SILR-NMF), in which we jointly consider structural incoherence and low-rank properties of data for image classification...
July 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30010571/hadamard-coding-for-supervised-discrete-hashing
#8
Gou Koutaki, Keiichiro Shirai, Mitsuru Ambai
In this paper, we propose a learning-based supervised discrete hashing method. Binary hashing is widely used for large-scale image retrieval as well as video and document searches because the compact binary code representation is essential for data storage and reasonable for query searches using bit-operations. The recently proposed supervised discrete hashing (SDH) method efficiently solves mixed-integer programming problems by alternating optimization and the discrete cyclic coordinate descent (DCC) method...
July 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30010570/low-rank-sparse-preserving-projections-for-dimensionality-reduction
#9
Luofeng Xie, Ming Yin, Xiangyun Yin, Yun Liu, Guofu Yin
Learning an efficient projection to map high-dimensional data into a lower-dimensional space is a rather challenging task in the community of pattern recognition and computer vision. Manifold learning is widely applied because it can disclose the intrinsic geometric structure of data. However, it only concerns the geometric structure and may lose its effectiveness in case of corrupted data. To address this challenge, we propose a novel dimensionality reduction method by combining manifold learning and low-rank sparse representation, termed low-rank sparse preserving projections (LSPP), which can simultaneously preserve the intrinsic geometric structure and learn a robust representation to reduce the negative effects of corruptions...
July 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30010569/color-contrast-preserving-decolorization
#10
Wei Wang, Zhengguo Li, Shiqian Wu
Decolorization is to convert a color image into a gray scale image while preserve image features like salient structure and chrominance contrast. The sign of the color contrast is crucial for the decolorization algorithm and is usually determined in existing works by giving a strict defined color order or twomode weak order. In this paper, a fast computation on color order is achieved via a simple global mapping which is introduced in a linear parametric model using an extended structure transfer filter. The values of the parameters are obtained via an elegant approximation method...
July 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30010568/video-captioning-by-adversarial-lstm
#11
Yang Yang, Jie Zhou, Jiangbo Ai, Yi Bin, Alan Hanjalic, Heng Tao Shen, Yanli Ji
In this paper, we propose a novel approach to video captioning based on adversarial learning and Long-Short Term Memory (LSTM). With this solution concept we aim at compensating for the deficiencies of LSTM-based video captioning methods that generally show potential to effectively handle temporal nature of video data when generating captions, but that also typically suffer from exponential error accumulation. Specifically, we adopt a standard Generative Adversarial Network (GAN) architecture, characterized by an interplay of two competing processes: a "generator", which generates textual sentences given the visual content of a video, and a "discriminator" which controls the accuracy of the generated sentences...
July 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30010567/low-rank-matrix-recovery-via-robust-outlier-estimation
#12
Xiaojie Guo, Zhouchen Lin
In practice, high-dimensional data are typically sampled from low-dimensional subspaces, but with intrusion of outliers and/or noises. Recovering the underlying structure and the pollution from the observations is of utmost importance to understanding the data. Besides properly modeling the subspace structure, how to handle the pollution is a core question regarding the recovery quality, the main origins of which include small dense noises and gross sparse outliers. Compared with the small noises, the outliers more likely ruin the recovery, as their arbitrary magnitudes can dominate the fidelity, and thus lead to misleading/erroneous results...
July 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30010566/self-similarity-constrained-sparse-representation-for-hyperspectral-image-super-resolution
#13
Xian-Hua Han, Boxin Shi, Yinqiang Zheng
Fusing a low-resolution hyperspectral image with the corresponding high-resolution multispectral image to obtain a high-resolution hyperspectral image is an important technique for capturing comprehensive scene information in both spatial and spectral domains. Existing approaches adopt sparsity promoting strategy, and encode the spectral information of each pixel independently, which results in noisy sparse representation. We propose a novel hyperspectral image super-resolution method via a self-similarity constrained sparse representation...
July 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30010565/more-is-better-precise-and-detailed-image-captioning-using-online-positive-recall-and-missing-concepts-mining
#14
Mingxing Zhang, Yang Yang, Hanwang Zhang, Yanli Ji, Heng Tao Shen, Tat-Seng Chua
Recently, a great progress in automatic image captioning has been achieved by using semantic concepts detected from the image. However, we argue that existing concepts-tocaption framework, in which the concept detector is trained using the image-caption pairs to minimize the vocabulary discrepancy, suffers from the deficiency of insufficient concepts. The reasons are two-fold: 1) the extreme imbalance between the number of occurrence positive and negative samples of the concept; and 2) the incomplete labelling in training captions caused by the biased annotation and usage of synonyms...
July 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30010564/weighted-large-margin-nearest-center-distance-based-human-depth-recovery-with-limited-bandwidth-consumption
#15
Meiyu Huang, Xueshuang Xiang, Yiqiang Chen, Da Fan
This paper proposes a weighted large margin nearest center (WLMNC) distance based human depth recovery method for tele-immersive video interaction systems with limited bandwidth consumption. In the remote stage, the proposed method highly compresses the depth data of the remote human into skeletal block structures by learning the WLMNC distance, which is equivalent to downsampling the human depth map at 64× the sampling rate. In the local stage, the method first recovers a rough human depth map based on a WLMNC distance augmented clustering approach and then obtains a fine depth map based on a rough depth guided autoregressive (AR) model to preserve depth discontinuities and suppress texture copy artifacts...
July 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30010563/hyperspectral-image-super-resolution-with-a-mosaic-rgb-image
#16
Ying Fu, Yinqiang Zheng, Hua Huang, Imari Sato, Yoichi Sato
Recently, many hyperspectral (HS) image superresolution methods that merge a low spatial resolution HS image and a high spatial resolution three-channel RGB image have been proposed in spectral imaging. A largely ignored fact is that most existing commercial RGB cameras capture high resolution images by a single CCD/CMOS sensor equipped with a color filter array (CFA). In this paper, we account for the common imaging mechanism of commercial RGB cameras, and propose to use a mosaic RGB image for HS image super-resolution, which prevents demosaicing error and thus its propagation into the HS image super-resolution results...
July 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30010562/attentive-linear-transformation-for-image-captioning
#17
Senmao Ye, Nian Liu, Junwei Han
We propose a novel attention framework called attentive linear transformation (ALT). Instead of learning the spatial or channel-wise attention in existing models, ALT learns to attend to the high-dimensional transformation matrix from the image feature space to the context vector space. Thus ALT can learn various relevant feature abstractions, including spatial attention, channel-wise attention and visual dependence. Besides, we propose a soft threshold regression to predict the attention probabilities for local regions...
July 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30010561/quality-of-experience-for-adaptive-streaming-videos-an-expectation-confirmation-theory-motivated-approach
#18
Zhengfang Duanmu, Kede Ma, Zhou Wang
The dynamic adaptive streaming over HTTP (DASH) provides an inter-operable solution to overcome volatile network conditions, but how the human visual quality-ofexperience (QoE) changes with time-varying video quality is not well-understood. Here, we build a large-scale video database of time-varying quality and design a series of subjective experiments to investigate how humans respond to compression level, spatial and temporal resolution adaptations. Our path-analytic results show that quality adaptations influence the QoE by modifying the perceived quality of subsequent video segments...
July 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30010560/multi-oriented-and-multi-lingual-scene-text-detection-with-direct-regression
#19
Wenhao He, Xu-Yao Zhang, Fei Yin, Cheng-Lin Liu
Multi-oriented and multi-lingual scene text detection plays an important role in computer vision area and is challenging due to the wide variety of text and background. In this paper, firstly we point out the two key tasks when extending CNN based object detection frameworks to scene text detection. The first task is to localize the text region by a down-sampled segmentation based module, and the second task is to regress the boundaries of text region determined by the first task. Secondly, we propose a scene text detection framework based on fully convolutional network (FCN) with a bi-task prediction module in which one is pixel-wise classification between text and non-text, and the other is pixel-wise regression to determine the vertex coordinates of quadrilateral text boundaries...
July 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30004876/photo-realistic-image-completion-via-dense-correspondence
#20
Jun-Jie Huang, Pier Luigi Dragotti
In this paper, we propose an image completion algorithm based on dense correspondence between the input image and an exemplar image retrieved from Internet. Contrary to traditional methods which register two images according to sparse correspondence, in this paper we propose a hierarchical PatchMatch method that progressively estimates a dense correspondence which is able to capture small deformations between images. The estimated dense correspondence has usually large occlusion areas that correspond to the regions to be completed...
July 11, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
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