Enhanced similarity for spectral clustering using local steering features

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In the field of clustering, spectral clustering (SC) has become an effective tool to analyze complex non-convex data using only pairwise affinity between the data points. Many novel affinity metrics have been proposed in the literature which use local features such as color, spatial coordinates, and texture. Some of these methods used SC for image segmentation [1, 2]. In this work, we have used the covariance matrix of the pixels in a patch and proposed an orientation based feature of a pixel called steering feature. This feature is robust and data-driven. The steering feature is used to enhance the construction of affinity metric for spectral clustering proposed by Shi and Malik [1]. Using the Nystrom framework [2] on images from BSD500 benchmark dataset, we have shown that the proposed affinity metric gives better result than Shi and Malik [1].




Chintalapati, L. S., & Rachakonda, R. S. (2019). Enhanced similarity for spectral clustering using local steering features. International Journal of Innovative Technology and Exploring Engineering, 8(12), 3231–3235. https://doi.org/10.35940/ijitee.L3073.1081219

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