Event
Ph.D. Dissertation Defense: Sumit Shekhar
Thursday, July 17, 2014
11:00 a.m.
4424 AV Williams Building
Maria Hoo
301 405 3681
mch@umd.edu
ANNOUNCEMENT: Ph.D. Dissertation Defense
Name: Sumit Shekhar
Committee:
Professor Rama Chellappa, Chair
Professor Ramani Duraiswami
Professor Behtash Babadi
Professor David Jacobs
Professor Amitabh Varshney (Dean's Representative)
Date/Time: Friday, July 17, 2014, 11:00am
Location: Room 4424 AV Williams Building
Title: Sparse Methods for Robust and Efficient Visual Recognition
Abstract:
Visual recognition has been a subject of extensive research in
computer vision. A vast literature exists on feature extraction and learning methods for recognition. However, due to large variations in visual data, robust
visual recognition is still an open problem. In recent years, sparse
representation-based methods have become popular for visual
recognition. By learning a compact dictionary of data and exploiting
the notion of sparsity, start-of-the-art results have been obtained on
many recognition tasks. However, existing data-driven sparse model
techniquesmay not be optimal for some challenging recognition
problems. In this dissertation, we consider some of these recognition
tasks and present approaches based on sparse coding for robust and
efficient recognition in such cases.
First we study the problem of low-resolution face recognition. This is
a challenging
problem, and methods have been proposed using super-resolution and
machine learning based techniques. However, these methods cannot
handle variations like illumination changes which can happen at low
resolutions, and degrade the performance. We propose a generative
approach for classifying the low resolution faces, by exploiting the
3D face models. Further, we propose a joint sparse coding framework
for robust classification at low resolutions. The effectiveness of the
method is demonstrated on different face datasets.
In the second part, we study a robust feature-level fusion method for multimodal
biometric recognition. Although score-level and decision-level fusion
methods exist in biometric literature, feature-level fusion is
challenging due to different output formats of biometric modalities.
In this work, we propose a novel sparse representation-based method
for multimodal fusion, and present experimental results for a large
multimodal dataset. Robustness to noise and occlusion are
demonstrated.
In the third part, we consider the problem of domain adaptation, where we want
to learn effective classifiers for cases where the test images come
from a different distribution than the training data. Typically, due
to high cost of human annotation, very few labeled samples are
available for images in the test domain. Specifically, we study the
problem of adapting sparse dictionary-based classification methods for
such cases. We describe a technique which jointly learns projections
of data in the two domains, and a latent dictionary which can
succinctly represent both domains in the projected lowdimensional
space. The proposed method is efficient and performs on par or better
than many competing state-of-the-art methods.
Lastly, we study an emerging analysis framework of sparse coding for
image classification. We show that the analysis sparse coding can give
similar performance as the typical synthesis sparse coding methods,
while being much faster at sparse encoding.
Committee:
Professor Rama Chellappa, Chair
Professor Ramani Duraiswami
Professor Behtash Babadi
Professor David Jacobs
Professor Amitabh Varshney (Dean's Representative)
Date/Time: Friday, July 17, 2014, 11:00am
Location: Room 4424 AV Williams Building
Title: Sparse Methods for Robust and Efficient Visual Recognition
Abstract:
Visual recognition has been a subject of extensive research in
computer vision. A vast literature exists on feature extraction and learning methods for recognition. However, due to large variations in visual data, robust
visual recognition is still an open problem. In recent years, sparse
representation-based methods have become popular for visual
recognition. By learning a compact dictionary of data and exploiting
the notion of sparsity, start-of-the-art results have been obtained on
many recognition tasks. However, existing data-driven sparse model
techniquesmay not be optimal for some challenging recognition
problems. In this dissertation, we consider some of these recognition
tasks and present approaches based on sparse coding for robust and
efficient recognition in such cases.
First we study the problem of low-resolution face recognition. This is
a challenging
problem, and methods have been proposed using super-resolution and
machine learning based techniques. However, these methods cannot
handle variations like illumination changes which can happen at low
resolutions, and degrade the performance. We propose a generative
approach for classifying the low resolution faces, by exploiting the
3D face models. Further, we propose a joint sparse coding framework
for robust classification at low resolutions. The effectiveness of the
method is demonstrated on different face datasets.
In the second part, we study a robust feature-level fusion method for multimodal
biometric recognition. Although score-level and decision-level fusion
methods exist in biometric literature, feature-level fusion is
challenging due to different output formats of biometric modalities.
In this work, we propose a novel sparse representation-based method
for multimodal fusion, and present experimental results for a large
multimodal dataset. Robustness to noise and occlusion are
demonstrated.
In the third part, we consider the problem of domain adaptation, where we want
to learn effective classifiers for cases where the test images come
from a different distribution than the training data. Typically, due
to high cost of human annotation, very few labeled samples are
available for images in the test domain. Specifically, we study the
problem of adapting sparse dictionary-based classification methods for
such cases. We describe a technique which jointly learns projections
of data in the two domains, and a latent dictionary which can
succinctly represent both domains in the projected lowdimensional
space. The proposed method is efficient and performs on par or better
than many competing state-of-the-art methods.
Lastly, we study an emerging analysis framework of sparse coding for
image classification. We show that the analysis sparse coding can give
similar performance as the typical synthesis sparse coding methods,
while being much faster at sparse encoding.
