A novel non-negative matrix factorization technique for decomposition of Chinese characters with application to secret sharing

Abstract

The decomposition of Chinese characters is difficult and has been rarely investigated in the literature. In this paper, we propose a novel non-negative matrix factorization (NMF) technique to decompose a Chinese character into several graphical components without considering the strokes of the character or any semantic or phonetic properties of the components. Chinese characters can usually be represented as binary images. However, traditional NMF is only suitable for representing general gray-level or color images. To decompose a binary image using NMF, we force all of the elements of the two matrices (obtained by factorizing the binary image/matrix to be decomposed) as close to 0 or 1 as possible. As a result, a Chinese character can be efficiently decomposed into several components, where each component is semantically unreadable. Moreover, our NMF-based Chinese character decomposition method is suitable for applications in visual secret sharing by distributing the shares (different character components) among multiple parties, so that only when the parties are taken together with their respective shares can the secret (the original Chinese character(s)) be reconstructed. Experimental results have verified the decomposition performance and the feasibility of the proposed method.

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“…Features must be carefully defined in advance before entering the classifier, while features needed to be redesigned as dataset changes. Some other algorithms combined several types of features into one feature vector, while dimensionality problem may emerge [12]. Soares et al [13] proposed a feature-based Bayesian extractor that build a 7-D feature vector for every pixel by Gabor wavelet transform.…”

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