Saliency detection using mutual consistency-guided spatial cues combination

Wang, Xin; Chen, Ning; Xu, Lizhong

doi:10.1016/j.infrared.2015.07.020

Cited by 13 publications

(10 citation statements)

References 33 publications

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“…Comparing these two kinds of weighting methods, the former is simple and fast to implement but lacks flexibility and easily results in a bad result; on the contrary, the latter has strong flexibility and adaptability and usually achieves better fusion effect. Therefore, here we adopt a mutual consistency guided fusion method, the effectiveness of which has been verified in our recent related work [19], to adaptively combine and LCM . Given the multiscale local sparse representation based saliency map and the local contrast measure based saliency map LCM , their mutual consistencies are first calculated:…”

Section: Adaptive Saliency Map Combinationmentioning

confidence: 92%

“…While we wish to preserve the fine image structures by selecting a large number of image scales, we also want to avoid decomposing connected regions into smaller noisy image patches. After deep consideration, in this work, a scheme is implemented based upon our previous work [19] that aims to decompose the infrared image with different numbers of image scales. The scales are given as follows:…”

Section: Scale Selectionmentioning

confidence: 99%

“…There are two ways for the weight calculation: one is the fixed weighting methods [20], and another is the adaptive weighting method [19]. Comparing these two kinds of weighting methods, the former is simple and fast to implement but lacks flexibility and easily results in a bad result; on the contrary, the latter has strong flexibility and adaptability and usually achieves better fusion effect.…”

Section: Adaptive Saliency Map Combinationmentioning

confidence: 99%

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

Wang

Chen

Zhang

et al. 2017

Mathematical Problems in Engineering

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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.

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Section: Adaptive Saliency Map Combinationmentioning

confidence: 92%

Section: Scale Selectionmentioning

confidence: 99%

Section: Adaptive Saliency Map Combinationmentioning

confidence: 99%

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

Wang

Chen

Zhang

et al. 2017

Mathematical Problems in Engineering

Self Cite

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show abstract

“…Following previous saliency models for infrared images [24]- [28], the proposed saliency detection method is first compared with 10 state-of-the-art saliency models: FT [8], CA [11], GS [38], BD [39], BSCA [21], MAP [40], MB+ [41], RS [17] and HCA [22]. And the experiments are carried out on three datasets, OSU, IMS, and DIP.…”

Section: E Comparison With State-of-the-art Saliency Modelsmentioning

confidence: 99%

“…Li et al [27] apply the gradient information on pedestrians to enhance the uniqueness of intensity, and combine it with multi-scale contrasts to obtain the final saliency. Wang et al [28] exploit a mutual consistency guided fusion strategy to adaptively combine the luminance contrast saliency map and contour saliency map for infrared images. Li et al [1] first calculate the background likelihood with background prior, and then use a Bayesian model to obtain the object prior based saliency.…”

Section: Introductionmentioning

confidence: 99%

Mutual Guidance-Based Saliency Propagation for Infrared Pedestrian Images

Zheng

Zhou

et al. 2019

IEEE Access

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Saliency detection is important in computer vision. However, most of the existing saliency models are designed for visible images. It is still a challenging problem to apply saliency detection algorithms on infrared images. In this paper, an effective propagation based saliency detection method for infrared pedestrian images is proposed. Firstly, based on the thermal characteristics of infrared images and thermal radiation models, a thermal analysis based saliency (TAS) is introduced. TAS measures the stableness of pedestrians based on maximally stable extremal regions, which is further improved by an intensity filter. Then, by taking into account the appearance characteristic of pedestrians, an appearance analysis weighted saliency (AAS) is proposed which combines the intensity and shape features of pedestrians to improve the intensity contrast. Finally, besides the commonly used intra-scale neighborhood, an inter-scale neighborhood is introduced to jointly construct a mutual guidance-based saliency propagation model. This model could simultaneously integrate the saliency features and improve the saliency performance. Two datasets DIP and IMS with 600 infrared pedestrian images are published. Then, extensive experiments and comparisons with state-of-the-art methods demonstrate the effectiveness of the proposed saliency method for infrared pedestrian images. INDEX TERMS Infrared images, pedestrian, saliency, propagation.

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Saliency Detection Model for Low Contrast Images Based on Amplitude Spectrum Analysis and Superpixel Segmentation

Yang

2016

Bio-Inspired Computing – Theories and Applications

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Saliency detection using mutual consistency-guided spatial cues combination

Cited by 13 publications

References 33 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

Mutual Guidance-Based Saliency Propagation for Infrared Pedestrian Images

Saliency Detection Model for Low Contrast Images Based on Amplitude Spectrum Analysis and Superpixel Segmentation

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