Gradient descent and coordinate descent are well understood in terms of their asymptotic behavior, but less so in a transient regime often used for approximations in machine learning. We investigate how proper initialization can have a profound effect on finding near-optimal solutions quickly. We show that a certain property of a data set, namely the boundedness of the correlations between eige...

In this paper, a new technique called Lophoscopic PCA is developed for recognition of partially occluded faces and faces with strong facial expression variations. As opposed to the PCA or the Eigenfeatures approaches, this method does not try to solve the face recognition problem neither from a holistic, nor a feature perspective; in fact it studies the problem from a near or pseudo-holistic pe...

In this paper, an efficient method using various histogrambased (high-dimensional) image content descriptors for automatically classifying general color photos into relevant categories is presented. Principal component analysis (PCA) is used to project the original high dimensional histograms onto their eigenspaces. Lower dimensional eigenfeatures are then used to train support vector machines ...

This paper presents a new approach for facial eigenfeature regularization and extraction. Image space spanned by the eigenvectors of the within-class scatter matrix is decomposed into three subspaces. Eigenfeatures are regularized differently in these subspaces based on an eigenspectrum model. This alleviates the problem of unreliable small and zero eigenvalues caused by noise and the limited n...

Robust semantic labeling of image regions is a basic problem in representing and retrieving image/video content. We propose an SVM-MRF framework to model features and their spatial distributions, leading towards a “semantic” representation. Eigenfeatures of Gabor wavelet features and Gaussian mixture model are used for feature clustering. Since similar feature vectors in one cluster can come fr...

We present a prediction and regularization strategy for alleviating the conventional problems of LDA and its variants. A procedure is proposed for predicting eigenvalues using few reliable eigenvalues from the range space. Entire eigenspectrum is divided using two control points, however, the effective low-dimensional discriminative vectors are extracted from the whole eigenspace. The estimated...

Abstract. Recent years showed a gradual transition from terrestrial to aerial survey thanks the development of UAV and sensors for it. Many sectors benefited by this change among which geological one; drones are flexible, cost-efficient can support outcrops surveying in many difficult situations such as inaccessible steep high rock faces. The experiences acquired survey, with total stations, GN...

this paper is based on a combination of the principal component analysis (pca), eigenface and support vector machines. using n-fold method and with respect to the value of n, any person’s face images are divided into two sections. as a result, vectors of training features and test features are obtain ed. classification precision and accuracy was examined with three different types of kernel and...

The development of any robotics application relying on visual information always raises the key question of what image features would be most informative about the motion to be performed. In this paper, we address this question in the context of visual robot positioning, where a neural network is used to learn the mapping between image features and robot movements, and global image descriptors ...

We describe a face modeling system which estimates complete facial structure and texture from a real-time video stream. The system begins with a face tracking algorithm which detects and stabilizes live facial images into a canonical 3D pose. The resulting canonical texture is then processed by a statistical model to lter imperfections and estimate unknown components such as missing pixels and ...