We propose a novel image set classification technique using linear regression models. Downsampled gallery image sets are interpreted as subspaces of a high dimensional space to avoid the computationally expensive training step. We estimate regression models for each test image using the class specific gallery subspaces. Images of the test set are then reconstructed using the regression models. Based on the minimum reconstruction error between the reconstructed and the original images, a weighted voting strategy is used to classify the test set. We performed extensive evaluation on the benchmark UCSD Honda, CMU Mobo and YouTube Celebrity datasets for face classification, and ETH-80 dataset for object classification. The results demonstrate that by using only a small amount of training data, our technique achieved competitive classification accuracy and superior computational speed compared with the state-of-the-art methods.
For non-parametric methods, several different metrics are used to determine the set to set similarity. @cite_31 , use the Euclidean distance between the sets' mean as the similarity metric. Cevikalp and Triggs @cite_0 present two models to learn set samples. The set to set distance using an affine hull model is called Affine Hull Image Set Distance (AHISD) while that using convex hull model is termed as the Convex Hull Image Set Distance (CHISD). @cite_10 used the mean image of image set and affine hull model to calculate the Sparse Approximated Nearest Points (SANP) for image sets in order to determine the distance between the training image set and test image set. Some non-parametric methods (e.g., @cite_15 , @cite_13 , @cite_29 , @cite_31 ) use a point on a geometric surface to represent the complete image set. The image set can also be represented either by a combination of linear subspaces or on a complex non-linear manifold. For linear subspaces, the cosine of the smallest angle between any vector in one subspace and any other vector in the other subspace is commonly used as the similarity metric between image sets.
@cite_13 @cite_15 @cite_29 @cite_0 @cite_31 @cite_10
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We propose an efficient and robust solution for image set classification. A joint representation of an image set is proposed which includes the image samples of the set and their affine hull model. The model accounts for unseen appearances in the form of affine combinations of sample images. To calculate the between-set distance, we introduce the Sparse Approximated Nearest Point (SANP). SANPs are the nearest points of two image sets such that each point can be sparsely approximated by the image samples of its respective set. This novel sparse formulation enforces sparsity on the sample coefficients and jointly optimizes the nearest points as well as their sparse approximations. Unlike standard sparse coding, the data to be sparsely approximated are not fixed. A convex formulation is proposed to find the optimal SANPs between two sets and the accelerated proximal gradient method is adapted to efficiently solve this optimization. We also derive the kernel extension of the SANP and propose an algorithm for dynamically tuning the RBF kernel parameter while matching each pair of image sets. Comprehensive experiments on the UCSD Honda, CMU MoBo, and YouTube Celebrities face datasets show that our method consistently outperforms the state of the art. We wish to match sets of images to sets of images where both sets are undergoing various distortions such as viewpoint and lighting changes. To this end we have developed a joint manifold distance (JMD) which measures the distance between two subspaces, where each subspace is invariant to a desired group of transformations, for example affine warping of the image plane. The JMD may be seen as generalizing invariant distance metrics such as tangent distance in two important ways. First, formally representing priors on the image distribution avoids certain difficulties, which in previous work have required ad-hoc correction. The second contribution is the observation that previous distances have been computed using what amounted to "home-grown" nonlinear optimizers, and that more reliable results can be obtained by using generic optimizers which have been developed in the numerical analysis community, and which automatically set the parameters which home-grown methods must set by art. The JMD is used in this work to cluster faces in video. Sets of faces detected in contiguous frames define the subspaces, and distance between the subspaces is computed using JMD. In this way the principal cast of a movie can be 'discovered' as the principal clusters. We demonstrate the method on a feature-length movie. We introduce a novel method for face recognition from image sets. In our setting each test and training example is a set of images of an individual's face, not just a single image, so recognition decisions need to be based on comparisons of image sets. Methods for this have two main aspects: the models used to represent the individual image sets; and the similarity metric used to compare the models. Here, we represent images as points in a linear or affine feature space and characterize each image set by a convex geometric region (the affine or convex hull) spanned by its feature points. Set dissimilarity is measured by geometric distances (distances of closest approach) between convex models. To reduce the influence of outliers we use robust methods to discard input points that are far from the fitted model. The kernel trick allows the approach to be extended to implicit feature mappings, thus handling complex and nonlinear manifolds of face images. Experiments on two public face datasets show that our proposed methods outperform a number of existing state-of-the-art ones. Local learning of sparse image models has proved to be very effective to solve inverse problems in many computer vision applications. To learn such models, the data samples are often clustered using the K-means algorithm with the Euclidean distance as a dissimilarity metric. However, the Euclidean distance may not always be a good dissimilarity measure for comparing data samples lying on a manifold. In this paper, we propose two algorithms for determining a local subset of training samples from which a good local model can be computed for reconstructing a given input test sample, where we consider the underlying geometry of the data. The first algorithm, called adaptive geometry-driven nearest neighbor search (AGNN), is an adaptive scheme, which can be seen as an out-of-sample extension of the replicator graph clustering method for local model learning. The second method, called geometry-driven overlapping clusters (GOCs), is a less complex nonadaptive alternative for training subset selection. The proposed AGNN and GOC methods are evaluated in image superresolution and shown to outperform spectral clustering, soft clustering, and geodesic distance-based subset selection in most settings.