In this paper we propose a novel method for learning a Mahalanobis distance measure to be used in the KNN classification algorithm. The algorithm directly maximizes a stochastic variant of the leave-one-out KNN score on the training set. It can also learn a low-dimensional linear embedding of labeled data that can be used for data visualization and fast classification. Unlike other methods, our classification model is non-parametric, making no assumptions about the shape of the class distributions or the boundaries between them. The performance of the method is demonstrated on several data sets, both for metric learning and linear dimensionality reduction.

/pdf/neighbourhood-components-analysis-2fl2gsy4cc.pdf

Neighbourhood Components Analysis

http://blog.ncue.edu.tw/sys/lib/read_attach.php?id=11931

Hiding data in images by simple LSB substitution

A direct LDA algorithm for high-dimensional data — with application to face recognition

Traditional image representations are not suited to conventional classification methods such as the linear discriminant analysis (LDA) because of the undersample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by the successes of the two-dimensional LDA (2DLDA) for face recognition, we develop a general tensor discriminant analysis (GTDA) as a preprocessing step for LDA. The benefits of GTDA, compared with existing preprocessing methods such as the principal components analysis (PCA) and 2DLDA, include the following: 1) the USP is reduced in subsequent classification by, for example, LDA, 2) the discriminative information in the training tensors is preserved, and 3) GTDA provides stable recognition rates because the alternating projection optimization algorithm to obtain a solution of GTDA converges, whereas that of 2DLDA does not. We use human gait recognition to validate the proposed GTDA. The averaged gait images are utilized for gait representation. Given the popularity of Gabor-function-based image decompositions for image understanding and object recognition, we develop three different Gabor-function-based image representations: 1) GaborD is the sum of Gabor filter responses over directions, 2) GaborS is the sum of Gabor filter responses over scales, and 3) GaborSD is the sum of Gabor filter responses over scales and directions. The GaborD, GaborS, and GaborSD representations are applied to the problem of recognizing people from their averaged gait images. A large number of experiments were carried out to evaluate the effectiveness (recognition rate) of gait recognition based on first obtaining a Gabor, GaborD, GaborS, or GaborSD image representation, then using GDTA to extract features and, finally, using LDA for classification. The proposed methods achieved good performance for gait recognition based on image sequences from the University of South Florida (USF) HumanID Database. Experimental comparisons are made with nine state-of-the-art classification methods in gait recognition.

/pdf/general-tensor-discriminant-analysis-and-gabor-features-for-oo5p96edbp.pdf

General Tensor Discriminant Analysis and Gabor Features for Gait Recognition

Many current face recognition algorithms perform badly when the lighting or pose of the probe and gallery images differ. In this paper we present a novel algorithm designed for these conditions. We describe face data as resulting from a generative model which incorporates both within-individual and between-individual variation. In recognition we calculate the likelihood that the differences between face images are entirely due to within-individual variability. We extend this to the non-linear case where an arbitrary face manifold can be described and noise is position-dependent. We also develop a "tied" version of the algorithm that allows explicit comparison across quite different viewing conditions. We demonstrate that our model produces state of the art results for (i) frontal face recognition (ii) face recognition under varying pose.

/pdf/probabilistic-linear-discriminant-analysis-for-inferences-2fkldrli4l.pdf

Probabilistic Linear Discriminant Analysis for Inferences About Identity

https://ir.nctu.edu.tw:443/bitstream/11536/30219/1/000088547100011.pdf

A new LDA-based face recognition system which can solve the small sample size problem

Secret image sharing

https://ir.nctu.edu.tw:443/bitstream/11536/29805/1/000166438300010.pdf

Image hiding by optimal LSB substitution and genetic algorithm

https://ir.nctu.edu.tw:443/bitstream/11536/27352/1/000186024700011.pdf

A simple and high-hiding capacity method for hiding digit-by-digit data in images based on modulus function

This study presents a user-friendly image-sharing method for easier management of the shadow images. The sharing of images among several branches (distributed disks) using the proposed method has several characteristics: 1) fast transmission among branches; 2) fault tolerance; 3) a secure storage system; 4) reduced chance of pirating of high-quality images; and 5) most importantly, the provision to each branch manager an easy-to-manage environment (because each shadow image looks like a shrunken version of the original image). The current approach still has the small-size and channel-independent properties of our previous work, namely, the size of each shadow image is only 1/r of that of the original image, and any r shadow images can be used for restoration (the restored image is independent of which r shadow images are used).

/pdf/an-image-sharing-method-with-user-friendly-shadow-images-2tb64pwp17.pdf

Ja-Chen Lin

Papers

A new LDA-based face recognition system which can solve the small sample size problem

Secret image sharing

Image hiding by optimal LSB substitution and genetic algorithm

A simple and high-hiding capacity method for hiding digit-by-digit data in images based on modulus function

An image-sharing method with user-friendly shadow images