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image Co-Saliency Detection Based on Clustering and Diffusion Process

Image Co-Saliency Detection Based on Clustering and Diffusio...

引用

30th IEEE conference on visual communications and image processing (VCIP)

作者： Hwang, Insung Park, Jae Sung Park, Gu Yong Cho, Nam Ik Seoul Natl Univ Dept Elect & Comp Engn INMC Seoul South Korea SAMSUNG Elect Co Ltd Visual Display Div Suwon South Korea

ISBN: (纸本)9781509053162

This paper presents a co-saliency detection algorithm based on clustering and diffusion process. For each image in a set, intra saliency maps are constructed from the measure of boundary and contrast priors. Then, segmented regions of all images are clustered based on features of colors, intra saliency and coherence of saliency. Co-saliency of each cluster is computed from combination of foreground probability and coherence of the cluster. The co-saliency of cluster is propagated over the segmented regions according to affinity between the cluster and segments. In addition, we adopt an intra image diffusion process from a graph with learned fully affinity in order to improve spatial consistency of co-saliency maps. Experimental results show that our algorithm yields better results compared to the state-of-the-art methods in terms of precision-recall curve, visual plausibility and computational cost.

关键词： Terms Co-saliency saliency foreground probability coherence of saliency diffusion process

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Q-DNN: A Quality-Aware Deep Neural Network for Blind Assessment of Enhanced images

Q-DNN: A Quality-Aware Deep Neural Network for Blind Assessm...

引用

30th IEEE conference on visual communications and image processing (VCIP)

作者： Wu, Qingbo Li, Hongliang Meng, Fanman Ngan, King N. Univ Elect Sci & Technol China Sch Elect Engn Chengdu Peoples R China Chinese Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781509053162

image enhancement is widely popular due to its capability of producing "better" visual quality for specific applications. Although many enhancement algorithms have been developed in recent years, the studies towards blind assessment of enhanced images are still very lacking. In this paper, we propose a data-driven blind image quality assessment (BIQA) method based on the quality-aware deep neural network (Q-DNN). Unlike the conventional hand-crafted features designed for measuring the degradation level of specific distortion types, a supervised learning model is utilized in our Q-DNN, which is capable of adaptively updating the feature extractor and quality regressor for describing the visual artifacts caused by different image enhancement tasks. Experimental results on two challenging enhanced image databases show that the proposed method is significantly superior to the state-of-the-art BIQA metrics.

关键词： Terms Enhanced images quality-aware deep neural network blind image quality assessment feature extractor quality regressor

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A CLASSIFICATION METHOD FOR ESTIMATING THE ILLUMINANT OF AN image

A CLASSIFICATION METHOD FOR ESTIMATING THE ILLUMINANT OF AN ...

引用

30th IEEE conference on visual communications and image processing (VCIP)

作者： Liu, Bozhi Qiu, Guoping Univ Nottingham Sch Comp Sci Nottingham NG7 2RD England

ISBN: (纸本)9781509053162

Identifying the light source of an image is important for image processing tasks such as colour correction and white point balancing. This is also known as colour constancy in computer vision. This paper presents a novel clustering classification colour constancy framework (the 4C method). Based on the assumption that similar illuminants will result in similar white point colours, we first use a clustering algorithm to group similar white point colours of the training samples into the same cluster. We then treat the images in the same cluster as belonging to the same illumination source and each cluster as one class of illuminants. The colour constancy problem, i.e., that of estimating the unknown illuminant of an image, becomes that of identifying which illuminant class (cluster) the images illuminant falling into. To achieve this, we derive an effective colour feature representation of the image and use a general classification algorithm to classify the image into one of the illuminant classes (clusters). We present experimental results on publicly available testing datasets and show that our new method is competitive to state of the art.

关键词： Colour Constancy Clustering Classification K-means Random Forest

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Complex Singular Value Decomposition based Stereoscopic image Quality Assessment

Complex Singular Value Decomposition based Stereoscopic Imag...

引用

30th IEEE conference on visual communications and image processing (VCIP)

作者： Wang, Xu Cao, Lei Ma, Lin Zhou, Yu Kwong, Sam Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Hamel Noahs Ark Lab Hong Kong Hong Kong Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781509053162

Designing a reliable and generic perceptual quality metric is a challenging issue in three-dimensional (3D) visual signal processing. Due to the limited knowledge on 3D perceptual, it is difficult to fuse the visual information of left and right views in an effective way. In this paper, we propose a complex singular value decomposition (CSVD) based stereoscopic image quality assessment (SIQA) metric. First, the corresponding blocks of the left/right view are grouped into complex representation (CR) block through the scale-invariant feature transform (SIFT) view matching process. Then we compute the CSVD coefficients of each CR block. Final, a CSVD based quality pooling stage is employed to predict the final visual quality of the distorted 3D image. Experimental results demonstrate that the proposed metric has good consistency with 3D perception of human.

关键词： Complex Singular Value Decomposition Stereoscopic image Quality Assessment

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A Structure-Preserving image Restoration Method With High-level Ensemble Constraints

A Structure-Preserving Image Restoration Method With High-le...

引用

30th IEEE conference on visual communications and image processing (VCIP)

作者： Chen, Rui Jia, Huizhu Xie, Xiaodong Wen, Gao Peking Univ Natl Engn Lab Video Technol Beijing Peoples R China

ISBN: (纸本)9781509053162

I n this paper, we present a new image restoration framework based on two high-level regularizations that can predict and preserve the better informative structures in the image. The sparse representation of a blurred image is first obtained to globally encode the salient structures by applying a group of coupled framelet filters. Then a physical meaning regularizer is derived to estimate the point spread function based on the frequency response characteristics of the image. Moreover, based on the operator of structure tensor, a novel nonlocal total variation as the regularizer is established to measure the image variation and non-local self-similarity. Finally, these two highlevel regularizers are integrated into an objective function to constrain the ill-posedness. Compared with the state-of-the-art restoration methods, our algorithm can not only suppress strong noises effectively but also recover the sharp structures from the severe and complex blurred images.

关键词： Terms image restoration structure tensor framelet filter point spread function nonlocal regularization

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A No-Reference Infrared image Sharpness Assessment Based on Singular Value Decomposition

A No-Reference Infrared Image Sharpness Assessment Based on ...

引用

30th IEEE conference on visual communications and image processing (VCIP)

作者： Li, Ying-Jie Di, Xiao-Guang Harbin Inst Technol Control & Simulat Ctr 92 West Dazhi St Harbin Peoples R China

ISBN: (纸本)9781509053162

Recent image sharpness metrics are usually proposed for visible light image and infrared image sharpness assessment is seldom discussed. As for infrared images, we usually concern more about the salient regions. Therefore, in this paper, a novel no-reference algorithm based on saliency detection (SD) and singular value decomposition (SVD) is proposed to assess infrared image sharpness. Gaussian blur is first used to build a reference image. Then salient regions are detected by combining the local mean and variation. Next, singular value decomposition-based metric is proposed to evaluate the variation between original image and reference image. The image quality score is finally obtained by using the five-parameter logistic regression. Experimental results show that the proposed method correlates well with the subjective quality evaluations of infrared images and is highly competitive with state-of-the-art visible light image sharpness metrics.

关键词： No-reference (NR) infrared image sharpness saliency detection singular value decomposition (SVD) logistic regression

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Multi-Level Convolutional Channel Features for Content-Based image Retrieval

Multi-Level Convolutional Channel Features for Content-Based...

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30th IEEE conference on visual communications and image processing (VCIP)

作者： Hu, Kun Dong, Yuan Bai, Hongliang Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing Peoples R China Beijing FaceAll Co Beijing Beijing Peoples R China

ISBN: (纸本)9781509053162

An effective content-based image retrieval (CBIR) system depends on the discriminative feature which represents an image. In this work, we explore deep convolutional features for a CBIR system. We first show the effectiveness of deep convolutional channel features for a CBIR system. Then we introduce a Multi Level Pooling method (MLP) to obtain object-aware features from convolutional layers and finally the features extracted from different layers are incorporated to a short representation vector. Through multiple experiments, we show that our approach can achieve state-of-art results on several benchmark retrieval datasets.

关键词： CBIR Convolutional Neural Network Deep learning Multi-level pooling PCA

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Exploiting Neural Models for No-reference image Quality Assessment

Exploiting Neural Models for No-reference Image Quality Asse...

引用

30th IEEE conference on visual communications and image processing (VCIP)

作者： Pan, Cenhui Xu, Yi Yan, Yichao Gu, Ke Yang, Xiaokang Shanghai Jiao Tong Univ Shanghai Peoples R China Shanghai Key Lab Digital Media Proc & Transmiss Shanghai Peoples R China Cooperat Medianet Innovat Ctr Shanghai Peoples R China

ISBN: (纸本)9781509053162

We propose an improved algorithm for no-reference image quality assessment (NR-IQA) using the convolutional neural network (CNN) and neural theory based saliency detection. Firstly, we extract non-overlapping patches from the input image. For each patch, we obtain the quality score by CNN network, which consists of seven layers and integrates feature learning and regression into image patch quality estimation. Considering that the patches attracting much attention take significant role in visual perception, an efficient technique based on free energy based neural model is used to detect the saliency map. This saliency map is then applied as a weighting mask to output the quality score of the whole image. Results of experiments show that our algorithm achieves state-of-the-art performance, as compared with the prevailing IQA methods.

关键词： No-Reference image Quality Assessment convolutional neural network free energy

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Robust View Interpolation with Mesh Cutting

Robust View Interpolation with Mesh Cutting

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30th IEEE conference on visual communications and image processing (VCIP)

作者： Jiang, Xiubao Wang, Ronggang Luo, Jiajia Wang, Zhenyu Gao, Wen Peking Univ Shenzhen Grad Sch Sch Elect & Comp Engn Beijing Peoples R China

ISBN: (纸本)9781509053162

Existing view interpolation methods like DIBR require accurate disparity map which greatly limits their applications. To this aim, we propose a novel mesh-based view interpolation algorithm capable of synthesizing visually coherent virtual views with rough disparity map estimated by stereo matching algorithms. We adopt an edge-aware mesh cutting method to explicitly handle occlusion and preserve sharp depth discontinuities. Experiments on Middlebury dataset and 3D-HEVC test sequences demonstrate that proposed method outperforms DIBR and state-of-the-art mesh-based view interpolation algorithm in terms of visual quality and PSNR.

关键词： view interpolation mesh cutting image-based rendering

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Perceptual Sparse Representation for Compressed Sensing of image

Perceptual Sparse Representation for Compressed Sensing of I...

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30th IEEE conference on visual communications and image processing (VCIP)

作者： Wu, Jian Wang, Yongfang Zhu, Kanghua Zhu, Yun Minist Educ Key Lab Adv Display & Syst Applicat Shanghai 200072 Peoples R China Shanghai Univ Sch Commun & Informat Engn Shanghai 200072 Peoples R China

ISBN: (纸本)9781509053162

Most traditional image coding schemes based on compressed sensing exploited the sparse domain in fixed bases and less consider the image non-stationary characteristic and human visual characteristic, which leads to poor performance of the reconstruction. In this paper, we proposed a novel sparse CS scheme combined with just-noticeable difference (JND) Model and random permutation. Firstly, the DCT-based JND profile has been utilized to remove the perceptual redundancies which also makes the signal sparser, then the random permutation is adopted to balance the sparsity of each block in image. Experimental results show that the proposed perceptual sparse algorithm outperforms some existing approaches, and it can achieve better subjective and objective image quality compared to other algorithms when the sampling rate is above 0.3.

关键词： Compressed Sensing Random Permutation Just-noticeable Distortion Sparsity Discrete Cosine Transform

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