As the popularity of touch-screen devices, understanding a user’s hand-drawn sketch has become an increasingly important research topic in artificial intelligence and computer vision. However, different from natural images, the hand-drawn sketches are often highly abstract, with sparse visual information and large intraclass variance, making the problem more challenging. In this work, we study ...

Abnormal activity detection in a video is a challenging and attractive task. In this paper, an approach using spatio-temporal feature and Laplacian sparse representation is proposed to tackle this problem. To detect the abnormal activity, we first detect interest points of a query video in the spatio-temporal domain. Then normalized combinational vectors, named HNF, are computed around the dete...

In this work, we propose a framework, dubbed Union-of-Subspaces SVM (US-SVM), to learn linear classifiers as sparse codes over a learned dictionary. In contrast to discriminative sparse coding with a learned dictionary, it is not the data but the classifiers that are sparsely encoded. Experiments in visual categorization demonstrate that, at training time, the joint learning of the classifiers ...

In this study, a novel feature learning method for synthetic aperture radar (SAR) image automatic target recognition is presented. It based on spatial pyramid matching (SPM), which represents an by concatenating the pooling vectors that are obtained from different resolution sub-regions. This exploits dependability of obtaining weighted features generated SPM The determined residuals sparse rep...

State-of-the-art image retrieval systems typically represent an image with a bag of low-level features. Since different images often exhibit different kinds of low-level characteristics, it is desirable to represent an image with multiple types of complementary features. The systems scalability is, however, significantly lowered when increasing the number of feature types, as the amount of data...

Title of Dissertation: SPARSE REPRESENTATION, DISCRIMINATIVE DICTIONARIES AND PROJECTIONS FOR VISUAL CLASSIFICATION Ashish Shrivastava, Doctor of Philosophy, 2015 Dissertation directed by: Professor Rama Chellappa Department of Electrical and Computer Engineering Developments in sensing and communication technologies have led to an explosion in the availability of visual data from multiple sour...

The discovery of key and distinctive parts is critical for scene parsing and understanding. However, it is a challenging problem due to the weakly supervised condition, i.e., no annotation for parts is available. To address above issues, we propose a unified framework for learning a representative and discriminative part model with deep convolutional features. Firstly, we employ selective searc...