Salient object detection via color and texture cues

Zhang, Qing; Jia-jun, Lin; Tao, Yanyun; Li, Wenju; Shi, Yanjiao

doi:10.1016/j.neucom.2017.02.064

Cited by 41 publications

(32 citation statements)

References 21 publications

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“…This study applies the precision ( ), recall ( ), accuracy ( ), and dice ( ) evaluation metrics to quantitatively score the binary segmentation results computed by the comparative algorithms. These evaluation metrics are widely used for judging the performance of binary segmentation algorithms [8,13,19,20,47,48,61,[83][84][85]. A binary segmentation algorithm with satisfactory performance has high precision, recall, accuracy, and dice values.…”

Section: Quantitative Evaluation Of Segmentation Resultsmentioning

confidence: 99%

“…However, many of the improved saliency segmentation algorithms still face difficulty when salient objects share similar color features with the background pixels. These algorithms often lack the ability to effectively handle complicated images with low contrast [18,20,37]. Complementing the methods of saliency computation with other useful analysis methods such as the morphological analysis can significantly improve image segmentation results.…”

Section: Saliency Based Segmentationmentioning

confidence: 99%

“…Brightness prior assumes that the brightness of background is higher than that of the salient object [13]. However, while methods based on these cognitive properties have performed well on certain images, they can fail to accurately detect salient objects that share uniform homogeneity with the background and for salient objects that touch the image border slightly [20]. The center prior assumes that images are acquired such that a salient object is often framed near the image center while background is distributed in the borders.…”

Section: Introductionmentioning

confidence: 99%

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Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

Olugbara

Taiwo

Heukelman

2018

Mathematical Problems in Engineering

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The prevalence of melanoma skin cancer disease is rapidly increasing as recorded death cases of its patients continue to annually escalate. Reliable segmentation of skin lesion is one essential requirement of an efficient noninvasive computer aided diagnosis tool for accelerating the identification process of melanoma. This paper presents a new algorithm based on perceptual color difference saliency along with binary morphological analysis for segmentation of melanoma skin lesion in dermoscopic images. The new algorithm is compared with existing image segmentation algorithms on benchmark dermoscopic images acquired from public corpora. Results of both qualitative and quantitative evaluations of the new algorithm are encouraging as the algorithm performs excellently in comparison with the existing image segmentation algorithms.

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Section: Quantitative Evaluation Of Segmentation Resultsmentioning

confidence: 99%

Section: Saliency Based Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

Olugbara

Taiwo

Heukelman

2018

Mathematical Problems in Engineering

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“…Rahtu et al, [10] proposed the system without any user interaction extracts foreground objects of interest. Foreground and background regions within and across video frames are separated by using proposed method which utilizes visual saliency information extracted from the input video.…”

Section: Background and Foreground Subtractionmentioning

confidence: 99%

Research on Salient Object Detection using Deep Learning and Segmentation Methods

2019

IJRTE

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Detecting and segmenting salient objects in natural scenes, often referred to as salient object detection has attracted a lot of interest in computer vision and recently various heuristic computational models have been designed. While many models have been proposed and several applications have emerged, yet a deep understanding of achievements and issues is lacking. The aim of this review work is to study about the details of methods in salient object detection. It not only focuses on the methods to detect saliency objects, but also reviews the works related to spatio temporal video attention detection technique in video sequences. It also discusses the open issues in terms of evaluation metrics and dataset bias in model performance and suggests future research directions. The evaluation metrics are classified into mean absolute error (MAE), Accuracy and Run-Time complexity

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“…Superpixel segmentation is to partition a still image into atomic segments of similar size and adhering to object boundaries, namely superpixels [ 1 , 2 , 3 , 4 ]. In recent decades, superpixel segmentation has been found to be a very useful preprocessing step in many computer vision tasks (e.g., object detection [ 5 , 6 , 7 ], image segmentation [ 8 , 9 , 10 ], visual saliency [ 11 ], and noise estimation [ 12 ]). This is mainly because superpixels improve the computational efficiency and robustness of subsequent applications by reducing the number of inputs and removing a large amount of redundant information.…”

Section: Introductionmentioning

confidence: 99%

Supervoxel Segmentation with Voxel-Related Gaussian Mixture Model

Ban

Zhong

Liu

2018

Sensors

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Extended from superpixel segmentation by adding an additional constraint on temporal consistency, supervoxel segmentation is to partition video frames into atomic segments. In this work, we propose a novel scheme for supervoxel segmentation to address the problem of new and moving objects, where the segmentation is performed on every two consecutive frames and thus each internal frame has two valid superpixel segmentations. This scheme provides coarse-grained parallel ability, and subsequent algorithms can validate their result using two segmentations that will further improve robustness. To implement this scheme, a voxel-related Gaussian mixture model (GMM) is proposed, in which each supervoxel is assumed to be distributed in a local region and represented by two Gaussian distributions that share the same color parameters to capture temporal consistency. Our algorithm has a lower complexity with respect to frame size than the traditional GMM. According to our experiments, it also outperforms the state-of-the-art in accuracy.

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Salient object detection via color and texture cues

Cited by 41 publications

References 21 publications

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

Research on Salient Object Detection using Deep Learning and Segmentation Methods

Supervoxel Segmentation with Voxel-Related Gaussian Mixture Model

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