Abstract
The graph theory-based graph cut algorithm is successfully applied in a wide range of problems in computer vision. The normalized graph cut algorithm is one of the efficient algorithms used for the detection of salient objects in one image. The efficiency of normalized graph cut algorithm has been improved by applying Lp1 norm-based Gaussian filter and median filter, with normalized cut algorithm, on individual color channels rather than the whole image. Different experiments are conducted to prove the efficiency of these applications by considering images from different standard image segmentation datasets like Berkley image segmentation dataset and COREL image segmentation dataset. The results of the proposed methods are compared with Otsu thresholding and C-means clustering algorithms. Three validity indices are used to quantify the results.
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Abinash, S., Pattnaik, S. (2021). A Hybrid Approach Based on Lp1 Norm-Based Filters and Normalized Cut Segmentation for Salient Object Detection. In: Patnaik, S., Yang, XS., Sethi, I. (eds) Advances in Machine Learning and Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-5243-4_64
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DOI: https://doi.org/10.1007/978-981-15-5243-4_64
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