Salient Object Detection via Structured Matrix Decomposition

Peng, Houwen; Li, Bing; Ling, Haibin; Hu, Weiming; Xiong, Wei; Maybank, Stephen J.

doi:10.1109/tpami.2016.2562626

Cited by 335 publications

(229 citation statements)

References 76 publications

(169 reference statements)

Supporting

Mentioning

226

Contrasting

Unclassified

Order By: Relevance

“…We compare it with ground truth data as well as eight state-of-the-art saliency detection algorithms. The first selected model, called a structured matrix decomposition model, was recently proposed by Peng et al [23] for salient object detection. It utilized a tree-structured sparsityinducing regularization and a Laplacian regularization to complete the saliency detection (we refer to this method as SMD).…”

Section: Resultsmentioning

confidence: 99%

Detecting Saliency in Infrared Images via Multiscale Local Sparse Representation and Local Contrast Measure

Wang

Chen

Zhang

et al. 2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

For infrared images, it is a formidable challenge to highlight salient regions completely and suppress the background noise effectively at the same time. To handle this problem, a novel saliency detection method based on multiscale local sparse representation and local contrast measure is proposed in this paper. The saliency detection problem is implemented in three stages. First, a multiscale local sparse representation based approach is designed for detecting saliency in infrared images. Using it, multiple saliency maps with various scales are obtained for an infrared image. These maps are then fused to generate a combined saliency map, which can highlight the salient region fully. Second, we adopt a local contrast measure based technique to process the infrared image. It divides the image into a number of image blocks. Then these blocks are utilized to calculate the local contrast to generate a local contrast measure based saliency map. In this map, the background noise can be suppressed effectually. Last, to make full use of the advantages of the above two saliency maps, we propose combining them together using an adaptive fusion scheme. Experimental results show that our method achieves better performance than several state-of-the-art algorithms for saliency detection in infrared images.

show abstract

Section: Resultsmentioning

confidence: 99%

Detecting Saliency in Infrared Images via Multiscale Local Sparse Representation and Local Contrast Measure

Wang

Chen

Zhang

et al. 2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…To further demonstrate the effectiveness of the proposed methods, we carry out several application tests including saliency detection [40] and keypoint matching [41]. In Figures 5 and 6, we present the results of application tests before and after using our method.…”

Section: Application Assessmentmentioning

confidence: 99%

Emerging From Water: Underwater Image Color Correction Based on Weakly Supervised Color Transfer

Guo

2018

IEEE Signal Process. Lett.

426

180

View full text Add to dashboard Cite

Abstract-Underwater vision suffers from severe effects due to selective attenuation and scattering when light propagates through water. Such degradation not only affects the quality of underwater images but limits the ability of vision tasks. Different from existing methods which either ignore the wavelength dependency of the attenuation or assume a specific spectral profile, we tackle color distortion problem of underwater image from a new view. In this letter, we propose a weakly supervised color transfer method to correct color distortion, which relaxes the need of paired underwater images for training and allows for the underwater images unknown where were taken. Inspired by Cycle-Consistent Adversarial Networks, we design a multiterm loss function including adversarial loss, cycle consistency loss, and SSIM (Structural Similarity Index Measure) loss, which allows the content and structure of the corrected result the same as the input, but the color as if the image was taken without the water. Experiments on underwater images captured under diverse scenes show that our method produces visually pleasing results, even outperforms the art-of-the-state methods. Besides, our method can improve the performance of vision tasks.

show abstract

“…DBS method was compared with 27 state-of-the-art methods in Section 7.2.1. 27 state-of-the-art methods are CB [34], FT [23], SEG [44], RC [14], SVO [17], LRR [39], SF [45], GS [37], CA [33], SS [47], HS [7], TD [48], MR [24], DRFI [25], PCA [41], HM [38], GC [36], MC [40], DSR [35], SBF [43], BD [42], SMD [46], BL [32], MCDL [9], MDF [8], LEGS [10], and RFCN [11]. These methods not only are very popular but also cover many types.…”

Section: Experiments On the New Datasetmentioning

confidence: 99%

Saliency Aggregation: Multifeature and Neighbor Based Salient Region Detection for Social Images

Liang

Lang

et al. 2018

Applied Computational Intelligence and Soft Computing

View full text Add to dashboard Cite

The popularity of social networks has brought the rapid growth of social images which have become an increasingly important image type. One of the most obvious attributes of social images is the tag. However, the sate-of-the-art methods fail to fully exploit the tag information for saliency detection. Thus this paper focuses on salient region detection of social images using both image appearance features and image tag cues. First, a deep convolution neural network is built, which considers both appearance features and tag features. Second, tag neighbor and appearance neighbor based saliency aggregation terms are added to the saliency model to enhance salient regions. The aggregation method is dependent on individual images and considers the performance gaps appropriately. Finally, we also have constructed a new large dataset of challenging social images and pixel-wise saliency annotations to promote further researches and evaluations of visual saliency models. Extensive experiments show that the proposed method performs well on not only the new dataset but also several state-of-the-art saliency datasets.

show abstract

Salient Object Detection via Structured Matrix Decomposition

Cited by 335 publications

References 76 publications

Detecting Saliency in Infrared Images via Multiscale Local Sparse Representation and Local Contrast Measure

Detecting Saliency in Infrared Images via Multiscale Local Sparse Representation and Local Contrast Measure

Emerging From Water: Underwater Image Color Correction Based on Weakly Supervised Color Transfer

Saliency Aggregation: Multifeature and Neighbor Based Salient Region Detection for Social Images

Contact Info

Product

Resources

About