Low-Rank Modeling and Its Applications in Image Analysis

Zhou, Xiaowei; Yang, Can; Zhao, Hongyu; Yu, Weichuan

doi:10.1145/2674559

Cited by 131 publications

(80 citation statements)

References 159 publications

(216 reference statements)

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“…The method has been exploited successfully in background and foreground separation [40], removing shadows from human face images, robust image alignment, robust image/video denoising [19] [41], and image analysis [18]. As an application of background modeling in video sequences [17], the extracted sparse component from a frame implies additional information that does not belong to other video frames.…”

Section: Related Workmentioning

confidence: 99%

RPCA-KFE: Key Frame Extraction for Video Using Robust Principal Component Analysis

Dang

Radha

2015

IEEE Trans. on Image Process.

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Key frame extraction algorithms consider the problem of selecting a subset of the most informative frames from a video to summarize its content. Several applications, such as video summarization, search, indexing, and prints from video, can benefit from extracted key frames of the video under consideration. Most approaches in this class of algorithms work directly with the input video data set, without considering the underlying low-rank structure of the data set. Other algorithms exploit the low-rank component only, ignoring the other key information in the video. In this paper, a novel key frame extraction framework based on robust principal component analysis (RPCA) is proposed. Furthermore, we target the challenging application of extracting key frames from unstructured consumer videos. The proposed framework is motivated by the observation that the RPCA decomposes an input data into: 1) a low-rank component that reveals the systematic information across the elements of the data set and 2) a set of sparse components each of which containing distinct information about each element in the same data set. The two information types are combined into a single l1-norm-based non-convex optimization problem to extract the desired number of key frames. Moreover, we develop a novel iterative algorithm to solve this optimization problem. The proposed RPCA-based framework does not require shot(s) detection, segmentation, or semantic understanding of the underlying video. Finally, experiments are performed on a variety of consumer and other types of videos. A comparison of the results obtained by our method with the ground truth and with related state-of-the-art algorithms clearly illustrates the viability of the proposed RPCA-based framework.

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Section: Related Workmentioning

confidence: 99%

RPCA-KFE: Key Frame Extraction for Video Using Robust Principal Component Analysis

Dang

Radha

2015

IEEE Trans. on Image Process.

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“…Theoretically, it works well as long as the value of noise is small enough. However, if the data are perturbed by high-level noise, it will be impossible to generate satisfactory reconstruction results since the traditional PCA could be easily corrupted by these gross errors [58]. It is well known that raw ( k , t )-space data are often obtained from the MRI machines under complex imaging conditions.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of Undersampled Big Dynamic MRI Data Using Non-Convex Low-Rank and Sparsity Constraints

Liu

Shi

et al. 2017

Sensors

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Dynamic magnetic resonance imaging (MRI) has been extensively utilized for enhancing medical living environment visualization, however, in clinical practice it often suffers from long data acquisition times. Dynamic imaging essentially reconstructs the visual image from raw (k,t)-space measurements, commonly referred to as big data. The purpose of this work is to accelerate big medical data acquisition in dynamic MRI by developing a non-convex minimization framework. In particular, to overcome the inherent speed limitation, both non-convex low-rank and sparsity constraints were combined to accelerate the dynamic imaging. However, the non-convex constraints make the dynamic reconstruction problem difficult to directly solve through the commonly-used numerical methods. To guarantee solution efficiency and stability, a numerical algorithm based on Alternating Direction Method of Multipliers (ADMM) is proposed to solve the resulting non-convex optimization problem. ADMM decomposes the original complex optimization problem into several simple sub-problems. Each sub-problem has a closed-form solution or could be efficiently solved using existing numerical methods. It has been proven that the quality of images reconstructed from fewer measurements can be significantly improved using non-convex minimization. Numerous experiments have been conducted on two in vivo cardiac datasets to compare the proposed method with several state-of-the-art imaging methods. Experimental results illustrated that the proposed method could guarantee the superior imaging performance in terms of quantitative and visual image quality assessments.

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“…First of all, imaging device and external ambient noise frequently acting the collection, conversion and transmission process of medical images leading the lower image quality of medical images making fuzzy boundary of tissue, difficult to identify subtle structure and medical diagnosis. 4,5 Furthermore, the minute variations of the same target in different modal and imaging angle will have great differences in the imaging results. In addition, target to be analyzed often overlaps the surrounding structures, with a relatively complex spatial location.…”

Section: Introductionmentioning

confidence: 99%

Segmentation method for medical image based on improved GrabCut

Jiang

et al. 2017

Int J Imaging Syst Tech

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Segmentation of medical images has a lot of interferences because of the low contrast and fuzzy boundaries. It's hard to get perfect effect using present image segmentation methods, so we put forward an improved algorithm based on GrabCut and Gaussian mixture model (GMM) in this paper in order to obtain simplify interactive operation and better segmentation precision. We extend the GrabCut approach in 2 respects. Firstly, the initial GMMs of foreground and background were obtained by training sets, which could improve the algorithm's convergence rate. Secondly, the segmentation was restricted by the figure of foreground from training. Experimental results showed that compared with the traditional GrabCut algorithm, our proposed algorithm can simplify interactive operation (t = 14.33, P < .01) and improve the segmentation speed (t = 16.77, P < .01). In addition, in respect of segmentation precision, our proposed algorithm was obviously better than the traditional algorithms such as Graph Cut, GrabCut and Lazy Snapping. (F = 149.546, P < .01). The improved algorithm we proposed in this manuscript is especially suitable for processing large‐scale medical images.

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Low-Rank Modeling and Its Applications in Image Analysis

Cited by 131 publications

References 159 publications

RPCA-KFE: Key Frame Extraction for Video Using Robust Principal Component Analysis

RPCA-KFE: Key Frame Extraction for Video Using Robust Principal Component Analysis

Reconstruction of Undersampled Big Dynamic MRI Data Using Non-Convex Low-Rank and Sparsity Constraints

Segmentation method for medical image based on improved GrabCut

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