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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/28103558/effective-multi-query-expansions-collaborative-deep-networks-based-feature-learning-for-robust-landmark-retrieval
#1
Yang Wang, Xuemin Lin, Lin Wu, Wenjie Zhang
Given a query photo issued by a user (q-user), the landmark retrieval is to return a set of photos with their landmarks similar to those of the query, while the existing studies on the landmark retrieval focus on exploiting geometries of landmarks for similarity matches between candidate photos and a query photo. We observe that the same landmarks provided by different users over social media community may convey different geometry information depending on the viewpoints and/or angles, and may subsequently yield very different results...
January 18, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28103557/data-dependent-label-distribution-learning-for-age-estimation
#2
Zhouzhou He, Xi Li, Zhongfei Zhang, Fei Wu, Xin Geng, Yaqing Zhang, Ming-Hsuan Yang, Yueting Zhuang
As an important and challenging problem in computer vision, face age estimation is typically cast as a classification or regression problem over a set of face samples with respect to several ordinal age labels, which have intrinsically cross-age correlations across adjacent age dimensions. As a result, such correlations usually lead to the age label ambiguities of the face samples. Namely, each face sample is associated with a latent label distribution that encodes the cross-age correlation information on label ambiguities...
January 18, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28103556/steerable-wavelet-machines-swm-learning-moving-frames-for-texture-classification
#3
Adrien Depeursinge, Zsuzsanna Puspoki, John Paul Ward, Michael Unser
We present texture operators encoding classspecific local organizations of image directions (LOID) in a rotation-invariant fashion. The LOIDs are key for visual understanding, and are at the origin of the success of the popular approaches such as local binary patterns (LBP) and the scaleinvariant feature transform (SIFT). Whereas LBPs and SIFT yield handcrafted image representations, we propose to learn dataspecific representations of the LOIDs in a rotation-invariant fashion. The image operators are based on steerable circular harmonic wavelets (CHW), offering a rich and yet compact initial representation for characterizing natural textures...
January 18, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28103555/robust-head-pose-estimation-based-on-partially-latent-mixture-of-linear-regressions
#4
Vincent Drouard, Radu Horaud, Antoine Deleforge, Sileye Ba, Georgios Evangelidis
Head-pose estimation has many applications, such as social event analysis, human-robot and human-computer interaction, driving assistance, and so forth. Head-pose estimation is challenging because it must cope with changing illumination conditions, variabilities in face orientation and in appearance, partial occlusions of facial landmarks, as well as bounding-box-to- face alignment errors. We propose tu use a mixture of linear regressions with partially-latent output. This regression method learns to map high-dimensional feature vectors (extracted from bounding boxes of faces) onto the joint space of head-pose angles and bounding-box shifts, such that they are robustly predicted in the presence of unobservable phenomena...
January 16, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28103554/robust-neighborhood-preserving-projection-by-nuclear-l2-1-norm-regularization-for-image-feature-extraction
#5
Zhao Zhang, Fanzhang Li, Mingbo Zhao, Li Zhang, Shuicheng Yan
We propose two nuclear- and L2,1-norm regularized two-dimensional neighborhood preserving projection (2DNPP) methods for extracting representative 2D image features. 2DNPP extracts neighborhood preserving features via minimizing the Frobenius norm based reconstruction error that is very sensitive to noise and outliers in data. To make the distance metric more reliable and robust, and encode the neighborhood reconstruction error more accurately, we minimize the nuclear- and L2,1-norm based reconstruction error respectively and measure it over each image...
January 16, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28103553/data-driven-affine-deformation-estimation-and-correction-in-cone-beam-computed-tomography
#6
Vincent Van Nieuwenhove, Jan De Beenhouwer, Thomas De Schryver, Luc Van Hoorebeke, Jan Sijbers
In Computed Tomography (CT), motion and deformation during the acquisition lead to streak artefacts and blurring in the reconstructed images. To remedy these artefacts, we introduce an efficient algorithm to estimate and correct for global affine deformations directly on the cone beam projections. The proposed technique is data-driven and thus removes the need for markers and/or a tracking system. A relationship between affine transformations and the cone beam transform is proven and used to correct the projections...
January 16, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092560/diffeomorphic-multi-frame-non-rigid-registration-of-cell-nuclei-in-2d-and-3d-live-cell-images
#7
Marco Tektonidis, Karl Rohr
To gain a better understanding of cellular and molecular processes it is important to quantitatively analyze the motion of subcellular particles in live cell microscopy image sequences. Since generally the subcellular particles move and cell nuclei move as well as deform, it is important to decouple the movement of particles from that of the cell nuclei using nonrigid registration methods. We have developed a diffeomorphic multi-frame approach for non-rigid registration of cell nuclei in 2D and 3D live cell fluorescence microscopy images...
January 16, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092559/learning-to-hash-with-optimized-anchor-embedding-for-scalable-retrieval
#8
Yuchen Guo, Guiguang Ding, Li Liu, Jungong Han, Ling Shao
Sparse representation and image hashing are powerful tools for data representation and image retrieval respectively. The combinations of these two tools for scalable image retrieval, i.e., Sparse Hashing (SH) methods, have been proposed in recent years and the preliminary results are promising. The core of those methods is a scheme that can efficiently embed the (highdimensional) image features into a low-dimensional Hamming space while preserving the similarity between features. Existing SH methods mostly focus on finding better sparse representations of images in the hash space...
January 16, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092558/discriminant-context-information-analysis-for-post-ranking-person-re-identification
#9
Jorge Garcia, Niki Martinel, Alfredo Gardel, Ignacio Bravo, Gian Luca Foresti, Christian Micheloni
Existing approaches for person re-identification are mainly based on creating distinctive representations or on learning optimal metrics. The achieved results are then provided in form of a list of ranked matching persons. It often happens that the true match is not ranked first but it is in the first positions. This is mostly due to the visual ambiguities shared between the true match and other "similar" persons. At the current state, there is a lack of a study of such visual ambiguities which limit the re-identification performance within the first ranks...
January 16, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092537/detail-enhanced-multi-scale-exposure-fusion
#10
Zhengguo Li, Zhe Wei, Changyun Wen, Jinghong Zheng
Multi-scale exposure fusion is an effective image enhancement technique for a high dynamic range (HDR) scene. In this paper, a new multi-scale exposure fusion algorithm is proposed to merge differently exposed low dynamic range (LDR) images by using the weighted guided image filter (WGIF) to smooth the Gaussian pyramids of weight maps for all the LDR images. Details in the brightest and darkest regions of the HDR scene are preserved better by the proposed algorithm without relative brightness change in the fused image...
January 16, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092554/graph-laplacian-regularization-for-image-denoising-analysis-in-the-continuous-domain
#11
Jiahao Pang, Gene Cheung
Inverse imaging problems are inherently underdetermined, and hence it is important to employ appropriate image priors for regularization. One recent popular prior- the graph Laplacian regularizer-assumes that the target pixel patch is smooth with respect to an appropriately chosen graph. However, the mechanisms and implications of imposing the graph Laplacian regularizer on the original inverse problem are not well understood. To address this problem, in this paper we interpret neighborhood graphs of pixel patches as discrete counterparts of Riemannian manifolds and perform analysis in the continuous domain, providing insights into several fundamental aspects of graph Laplacian regularization for image denoising...
January 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092553/deep-aesthetic-quality-assessment-with-semantic-information
#12
Yueying Kao, Ran He, Kaiqi Huang
Human beings often assess the aesthetic quality of an image coupled with the identification of the image's semantic content. This paper addresses the correlation issue between automatic aesthetic quality assessment and semantic recognition. We cast the assessment problem as the main task among a multitask deep model, and argue that semantic recognition task offers the key to address this problem. Based on convolutional neural networks, we employ a single and simple multi-task framework to efficiently utilize the supervision of aesthetic and semantic labels...
January 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092552/discriminative-elastic-net-regularized-linear-regression
#13
Zheng Zhang, Zhihui Lai, Yong Xu, Ling Shao, Jian Wu, Guo-Sen Xie
In this paper, we aim at learning compact and discriminative linear regression models. Linear regression has been widely used in different problems. However, most of the existing linear regression methods exploit the conventional zeroone matrix as the regression targets, which greatly narrows the flexibility of the regression model. Another major limitation of theses methods is that the learned projection matrix fails to precisely project the image features to the target space due to their weak discriminative capability...
January 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092551/hierarchical-image-segmentation-based-on-iterative-contraction-and-merging
#14
Jia-Hao Syu, Sheng-Jyh Wang, Li-Chun Wang
In this paper, we propose a new framework for hierarchical image segmentation based on iterative contraction and merging (ICM). In the proposed framework, we treat the hierarchical image segmentation problem as a sequel of optimization problems, with each optimization process being realized by a contraction-and-merging process to identify and merge the most similar data pairs at the current resolution. At the beginning, we perform pixel-based contraction and merging to quickly combine image pixels into initial region-elements with visually indistinguishable intra-region color difference...
January 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092550/-l_0-gradient-projection
#15
Shunsuke Ono
Minimizing L0 gradient, the number of the nonzero gradients of an image, together with a quadratic datafidelity to an input image has been recognized as a powerful edge-preserving filtering method. However, the L0 gradient minimization has an inherent difficulty: a user-given parameter controlling the degree of flatness does not have a physical meaning since the parameter just balances the relative importance of the L0 gradient term to the quadratic data-fidelity term. As a result, the setting of the parameter is a troublesome work in the L0 gradient minimization...
January 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092549/learning-short-binary-codes-for-large-scale-image-retrieval
#16
Li Liu, Mengyang Yu, Ling Shao
Large-scale visual information retrieval has become an active research area in this big data era. Recently, hashing/ binary coding algorithms prove to be effective for scalable retrieval applications. Most existing hashing methods require relatively long binary codes (i.e., over hundreds of bits, sometimes even thousands of bits) to achieve reasonable retrieval accuracies. However, for some realistic and unique applications such as on wearable or mobile devices, only short binary codes can be used for efficient image retrieval due to the limitation of computational resources or bandwidth on these devices...
January 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092548/linear-spectral-clustering-superpixel
#17
Jiansheng Chen, Zhengqin Li, Bo Huang
In this paper, we present a superpixel segmentation algorithm called linear spectral clustering (LSC), which is capable of producing superpixels with both high boundary adherence and visual compactness for natural images with low computational costs. In LSC, a normalized cuts based formulation of image segmentation is adopted using a distance metric that measures both the color similarity and the space proximity between image pixels. However, rather than directly using the traditional eigen-based algorithm, we approximate the similarity metric through a deliberately designed kernel function such that pixel values can be explicitly mapped to a high-dimensional feature space...
January 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092547/asymmetrically-compressed-stereoscopic-3d-videos-quality-assessment-and-rate-distortion-performance-evaluation
#18
Jiheng Wang, Shiqi Wang, Zhou Wang
Objective quality assessment of stereoscopic 3D video is challenging but highly desirable, especially in the application of stereoscopic video compression and transmission, where useful quality models are missing that can guide the critical decision making steps in the selection of mixed-resolution coding, asymmetric quantization, and pre- and post-processing schemes. Here we first carry out subjective quality assessment experiments on two databases that contain various asymmetrically compressed stereoscopic 3D videos obtained from mixed-resolution coding, asymmetric transform-domain quantization coding, their combinations, and multiple choices of postprocessing techniques...
January 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092557/single-image-super-resolution-using-global-regression-based-on-multiple-local-linear-mappings
#19
Jae-Seok Choi, Munchurl Kim
Super-resolution (SR) has become more vital, because of its capability to generate high-quality ultra-high definition (UHD) high-resolution (HR) images from low-resolution (LR) input images. Conventional SR methods entail high computational complexity, which makes them difficult to be implemented for up-scaling of full-high-definition (FHD) input images into UHD-resolution images. Nevertheless, our previous super-interpolation (SI) method showed a good compromise between PSNR performances and computational complexity...
January 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092556/study-of-saliency-in-objective-video-quality-assessment
#20
Wei Zhang, Hantao Liu
Reliably predicting video quality as perceived by humans remains challenging and is of high practical relevance. A significant research trend is to investigate visual saliency and its implications for video quality assessment. Fundamental problems regarding how to acquire reliable eye-tracking data for the purpose of video quality research and how saliency should be incorporated in objective video quality metrics (VQMs) are largely unsolved. In this paper, we propose a refined methodology for reliably collecting eye-tracking data, which essentially eliminates bias induced by each subject having to view multiple variations of the same scene in a conventional experiment...
January 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
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