Co-segmentation via visualization

Kamranian, Zahra; Tombari, Federico; Nilchi, Ahmad Reza Naghsh; Monadjemi, Amirhassan; Navab, Nassir

doi:10.1016/j.jvcir.2018.05.014

Cited by 4 publications

(3 citation statements)

References 45 publications

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“…The performance of the proposed method is evaluated and compared with different previously proposed techniques (Meng et al, 2012;Dai et al, 2013;Faktor and Irani, 2013;Jerripothula et al, 2014;Jerripothula et al, 2015;Lee et al, 2015;Kamranian et al, 2018) using IoU metric. This metric is calculated as the intersection area of the segmented foreground and the ground truth to their union.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…In both tables, the average of the IoU is calculated for each class because each dataset has a massive number of images. It will be noticed that some results of methods (Meng et al, 2016;Kamranian et al, 2018) are missed; this is because the results are gotten from their original paper. However, the results of the other methods are obtained from (Lu et al, 2019).…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Recently, Convolutional Neural Networks (CNNs) have been used in the co-segmentation. Kamranian et al (2018) propose CNN-based Feature Visualization (CNN-FV) for detecting the foreground and use this information in the energy minimization function to improve the co-segmentation results. Gong et al (2020) propose a co-segmentation technique that calculates the visual correlation between images based on the co-attention computation block and Siamese network.…”

Section: Literature Reviewmentioning

confidence: 99%

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Unsupervised Cosegmentation Model based on Saliency Detection and Optimized Hue Saturation Value Features of Superpixels

Shoitan¹,

Moussa²

2021

Journal of Computer Science

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Segmenting out the foreground from a single image remains a challenge in computer vision. Image co-segmentation has been used recently to alleviate the single image segmentation by exploiting the information of the common object to be segmented from a group of images having the same object. This research proposes an unsupervised co-segmentation technique based on saliency detection and optimized features of the histogram of Hue, Saturation and Value (HSV) of the superpixels. The proposed method is formulated as the conventional Markov Random Field (MRF) segmentation model with an added co segmentation constraint. First, an initial segmentation is extracted based on a saliency technique. Afterward, a Particle Swarm algorithm (PSO) is utilized to select, iteratively, some superpixels on the inner and outer boundary of the initial segmentation to be a foreground or a background according to the optimized energy function. PSO is guided by the HSV dominant colors of the image class and the superpixels around the foreground to decide if a certain superpixel is related to the foreground or the background. The proposed method is evaluated by two datasets: iCoseg and MSRC, along with comparisons to the results of using ten conventional methods based on the Intersection over Union (IoU) metric. The experimental results demonstrate that the proposed method can segment the object successfully and accurately more than the traditional cosegmentation methods, even with a cluttered background.

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