Enhanced Similarity for Spectral Clustering using Local Steering Features
Lalith Srikanth Chintalapati1, Raghunatha Sarma Rachakonda2
1LalithSrikanthChintalapati, Department of Mathematics and Computer Science, Sri Sathya Sai Institute of Higher Learning, Puttaparthi, India.
2Raghunatha Sarma Rachakonda, Department of Mathematics and Computer Science, Sri Sathya Sai Institute of Higher Learning, Puttaparthi, India.
Manuscript received on September 13, 2019. | Revised Manuscript received on 22 September, 2019. | Manuscript published on October 10, 2019. | PP: 3231-3235 | Volume-8 Issue-12, October 2019. | Retrieval Number: L30731081219/2019©BEIESP | DOI: 10.35940/ijitee.L3073.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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 . Using the Nystrom framework  on images from BSD500 benchmark dataset, we have shown that the proposed affinity metric gives better result than Shi and Malik .
Keywords: Spectral Clustering, Affinity matrix, Steering Kernel Regression, Nystrom method.
Scope of the Article: Clustering