Two-dimensional approximately harmonic projection for gait recognition

This paper presents a two-dimensional approximately harmonic projection (2DAHP) algorithm for gait recognition. 2DAHP is originated from the approximately harmonic projection (AHP), while 2DAHP offers some advantages over AHP. 1) 2DAHP can preserve the local geometrical structure and cluster structure of image data as AHP. 2) 2DAHP encodes images as matrices or second-order tensors rather than one-dimensional vectors, so 2DAHP can keep the correlation among different coordinates of image data. 3) 2DAHP avoids the singularity problem suffered by AHP. 4) 2DAHP runs faster than AHP. Extensive experiments on gait recognition show the effectiveness and efficiency of the proposed method

Tensor Self-Organizing Map

九州工業大学博士学位論文 学位記番号：生工博甲第272号 学位授与年月日：平成28年6月30日第1章 序論|第2章 基礎知識|第3章 Tensor SOM:TSOM|第4章 人工データを用いたTSOMの検証|第5章 TSOMによるアンケートデータ解析|第6章 TSOMのバリエーション|第7章 討論|第8章 総括九州工業大学平成28年

Classification with the matrix-variate-t distribution

Matrix-variate distributions can intuitively model the dependence structure of matrix-valued observations that arise in applications with multivariate time series, spatio-temporal or repeated measures. This paper develops an Expectation-Maximization algorithm for discriminant analysis and classification with matrix-variate t-distributions. The methodology shows promise on simulated datasets or when applied to the forensic matching of fractured surfaces or the classification of functional Magnetic Resonance, satellite or hand gestures images