Spectral clustering based on high‐frequency texture components for face datasets

Abstract

Spectral clustering is one of the most widely used technologies for clustering tasks, which represents data as a weighted graph, and aims to find an appropriate way to cut the graph apart in order to categorize the raw data. The pivotal step of spectral clustering is to find out the accurate information to estimate the relationship of pairwise data, based on which a graph can be constructed. According to the cognition that different faces are distinguished by the edge contour which can be represented by high-frequency texture components, a novel spectral clustering algorithm via high-frequency signal of human face, named highfrequency spectral clustering (HFSC) is proposed. In HFSC, first the local high-frequency texture components are extracted from samples. Then the relationship of pairwise samples can be estimated with the degree of correlation, which is produced from the image high-frequency information. The graph will be set up with the correlation information. Subsequently the graph cut will be implemented to achieve the final clustering results. Experimental results show that this algorithm outperforms the state-of-the-art clustering methods on several datasets.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

An Effective Clustering Algorithm for the Low-Quality Image of Integrated Circuits via High-Frequency Texture Components Extraction

“…When different samples are corrupted by large area pixel loss, traditional methods would probably categorize them into the same cluster due to the high energy of noise. Inspired by our previous work [26] that the HFTC features selected from face images can well maintain the valuable morphology information while the noise is greatly weakened, we believe the key knowledge of the low-quality ICs' images could be better utilized through the HFTC extraction. Specifically, the valuable information of the ICs' images exists in the layout of the elements, which can be effectively retained by HFTC extraction.…”

“…One simple and efficient way for implementation is to extract the high-frequency information. In our previous work [26], the morphology characteristic of the human face image is effectively extracted and utilized for distinguishing different individuals. However, the key information of the IC's images is the layouts and shapes of the elements.…”

“…In the proposed approach, the unlabeled partial images of all object chips are assembled as a dataset. Firstly, the key information, called the High-Frequency Texture Components (HFTC) [26], of images will be extracted. Subsequently, a graph of data points will be constructed with an adjacency matrix calculated by the correlationship among the HFTC of all samples.…”