Object tracking is a challenging task in many computer vision applications due to occlusion, scale variation and background clutter, etc. In this paper, we propose a tracking algorithm by combining discriminative global and generative multi-scale local models. In the global model, we teach a classifier with sparse discriminative features to separate the target object from the background based o...

Part-based representation has been proven to be effective for a variety of visual applications. However, automatic discovery of discriminative parts without object / part-level annotations is challenging. This paper proposes a discriminative mid-level representation paradigm based on the responses of a collection of part detectors, which only requires the imagelevel labels. Towards this goal, w...

Bag-of-Words approach has played an important role in recent works for image classification. In consideration of efficiency, most methods use kmeans clustering to generate the codebook. The obtained codebooks often lose the cluster size and shape information with distortion errors and low discriminative power. Though some efforts have been made to optimize codebook in sparse coding, they usuall...

Title of dissertation: SPARSE DICTIONARY LEARNING AND DOMAIN ADAPTATION FOR FACE AND ACTION RECOGNITION Qiang Qiu, Doctor of Philosophy, 2013 Dissertation directed by: Professor Rama Chellappa Department of Computer Science New approaches for dictionary learning and domain adaptation are proposed for face and action recognition. We first present an approach for dictionary learning of action att...

In this paper, we proposed a novel sparse coding algorithm by using the class labels to constrain the learning of codebook and sparse code. We not only use the class label to train the classifier, but also use it to construct class conditional codewords to make the sparse code as discriminative as possible. We first construct ideal sparse codes with regarding to the class conditional codewords,...

No single universal image set representation can efficiently encode all types of image set variations. In the absence of expensive validation data, automatically ranking representations with respect to performance is a challenging task. We propose a sparse kernel learning algorithm for automatic selection and integration of the most discriminative subset of kernels derived from different image ...

In this paper, we proposed a new semi-supervised multi-manifold learning method, called semisupervised sparse multi-manifold embedding (S3MME), for dimensionality reduction of hyperspectral image data. S3MME exploits both the labeled and unlabeled data to adaptively find neighbors of each sample from the same manifold by using an optimization program based on sparse representation, and naturall...