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Classification based on image sets has recently attracted great research interest as it holds more promise than single image based classification. In this paper, we propose an efficient and robust algorithm for image set classification. An image set is represented as a triplet: a number of image samples, their mean and an affine hull model. The affine hull(More)
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(More)
Detection of salient regions in images is useful for object based image retrieval and browsing applications. This task can be done using methods based on the human visual attention model [1], where feature maps corresponding to color, intensity and orientation capture the corresponding salient regions. In this paper, we propose a strategy for combining the(More)
We formulate the problem of salient object detection in images as an automatic labeling problem on the vertices of a weighted graph. The seed (labeled) nodes are first detected using Markov random walks performed on two different graphs that represent the image. While the global properties of the image are computed from the random walk on a complete graph,(More)
Image-To-Class (I2C) distance is first used in Naive-Bayes Nearest-Neighbor (NBNN) classifier for image classification and has successfully handled datasets with large intra-class variances. However, the performance of this distance relies heavily on the large number of local features in the training set and test image, which need heavy computation cost for(More)
One of the key challenges in human action recognition from video sequences is how to model an action sufficiently. Therefore, in this paper we propose a novel motion-based representation called Motion Context (MC), which is insensitive to the scale and direction of an action, by employing image representation techniques. A MC captures the distribution of(More)
We propose ‘Contour Code’, a novel representation and binary hash table encoding for multispectral palmprint recognition. We first present a reliable technique for the extraction of a region of interest (ROI) from palm images acquired with non-contact sensors. The Contour Code representation is then derived from the Nonsubsampled Contourlet(More)
We formulate the problem of salient region detection in images as Markov random walks performed on images represented as graphs. While the global properties of the image are extracted from the random walk on a complete graph, the local properties are extracted from a k-regular graph. The most salient node is selected as the one which is globally most(More)
Detecting visually attentive regions of an image is a challenging but useful issue in many multimedia applications. In this paper, we describe a method to extract visual attentive regions in images using subspace estimation and analysis techniques. The image is represented in a 2D space using polar transformation of its features so that each region in the(More)