Divergence based SLIC

Gupta, Ashish Kumar; Seal, Ayan; Khanna, Pritee

doi:10.1049/el.2019.1092

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…The BSDS500 dataset contains 200 test, 200 train and 100 validation images. It is being used as a benchmark dataset for several segmentation algorithms like [55][56][57]. The dataset contains very distinct landscapes and sceneries.…”

Section: Resultsmentioning

confidence: 99%

SEEK: A Framework of Superpixel Learning with CNN Features for Unsupervised Segmentation

et al. 2020

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Supervised semantic segmentation algorithms have been a hot area of exploration recently, but now the attention is being drawn towards completely unsupervised semantic segmentation. In an unsupervised framework, neither the targets nor the ground truth labels are provided to the network. That being said, the network is unaware about any class instance or object present in the given data sample. So, we propose a convolutional neural network (CNN) based architecture for unsupervised segmentation. We used the squeeze and excitation network, due to its peculiar ability to capture the features’ interdependencies, which increases the network’s sensitivity to more salient features. We iteratively enable our CNN architecture to learn the target generated by a graph-based segmentation method, while simultaneously preventing our network from falling into the pit of over-segmentation. Along with this CNN architecture, image enhancement and refinement techniques are exploited to improve the segmentation results. Our proposed algorithm produces improved segmented regions that meet the human level segmentation results. In addition, we evaluate our approach using different metrics to show the quantitative outperformance.

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Section: Resultsmentioning

confidence: 99%

SEEK: A Framework of Superpixel Learning with CNN Features for Unsupervised Segmentation

et al. 2020

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“…A superpixel segmentation algorithm is expected to address the diverse requirements comprising of boundary adherence, compactness, connectivity, and computational efficiency to be useful as a preprocessing step [1,[17][18][19]. These algorithms can be broadly categorized as density-based [20], graph-based [21], contour evolution [22], and cluster-based superpixels [1,23,24]. While most approaches have been utilized in only some specific applications, simple linear iterative clustering (SLIC) [1] has been widely applied as a preprocessor to many important computer vision problems.…”

Section: Introductionmentioning

confidence: 99%

AWkS: adaptive, weighted k-means-based superpixels for improved saliency detection

Gupta

Seal

Khanna

et al. 2020

Pattern Anal Applic

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Clustering inspired superpixel algorithms perform a restricted partitioning of an image, where each visually coherent region containing perceptually similar pixels serves as a primitive in subsequent processing stages. Simple linear iterative clustering (SLIC) has emerged as a standard superpixel generation tool due to its exceptional performance in terms of segmentation accuracy and speed. However, SLIC applies a manually adjusted distance measure for dis-similarity computation which directly affects the quality of superpixels. In this work, self-adjustable distance measures are adapted from the weighted k-means clustering (W-k-means) for generating superpixel segmentation. In the proposed distance measures, an adaptive weight associated with each variable reflects its relevance in the clustering process. Intuitively, the variable weights correspond to the normalization terms in SLIC that affect the trade-off between superpixels boundary adherence and compactness. Weights that influence consistency in superpixel generation are automatically updated. The variable weights update is accomplished during optimization with a closed-form solution based on the current image partition. The proposed adaptive, W-k-means-based superpixels (AWkS) experimented on three benchmarks under different distance measure outperform the conventional SLIC algorithm with respect to various boundary adherence metrics. Finally, the effectiveness of the AWkS over SLIC is demonstrated for saliency detection.

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Skin cancer detection using Kernel Fuzzy C-means and Developed Red Fox Optimization algorithm

Yang

et al. 2022

Biomedical Signal Processing and Control

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Divergence based SLIC

Cited by 6 publications

References 13 publications

SEEK: A Framework of Superpixel Learning with CNN Features for Unsupervised Segmentation

SEEK: A Framework of Superpixel Learning with CNN Features for Unsupervised Segmentation

AWkS: adaptive, weighted k-means-based superpixels for improved saliency detection

Skin cancer detection using Kernel Fuzzy C-means and Developed Red Fox Optimization algorithm

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