Zhou, Yin. (University of Delaware) “Sparse signal processing for machine learning and computer vision” (2015)

You are here

Inside Signal Processing Newsletter Home Page

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

10 years of news and resources for members of the IEEE Signal Processing Society

Zhou, Yin. (University of Delaware) “Sparse signal processing for machine learning and computer vision” (2015)

Zhou, Yin. (University of Delaware) “Sparse signal processing for machine learning and computer vision”, Advisor: Barner, Kenneth E.

Signal sparse representation solves inverse problems to find succinct expressions of data samples as a linear combination of a few atoms in the dictionary or codebook. This model has proven effective in image restoration, denoising, inpainting, compression, pattern classification and automatic unsupervised feature learning.

Many classical sparse coding algorithms have exorbitant computational complexity in solving the sparse solution, which hinders their applicability in real-world large-scale machine learning and computer vision problems. In this dissertation, the author will first present a family of locality-constrained dictionary learning algorithms, which can be seen as a special case of sparse coding. Compared to classical sparse coding, locality-constrained coding has closed-form solution and is much more computationally efficient. In addition, the locality-preserving property enables the newly proposed algorithms to better exploit the geometric structures of data manifold. Experimental results demonstrate that our algorithms are capable of achieving superior classification performance with substantially higher efficiency, compared to sparse-coding based dictionary algorithms.

Sparse coding is an effective building block of learning visual features. A good feature representation is critical for machine learning algorithms to achieve satisfactory results. In recent years, unsupervised feature learning has received increasing research interest in various computer vision and pattern recognition problems. Unlike humanengineered feature extractors that typically require domain knowledge and a large amount of labeled data, unsupervised learning algorithms are generic and designed to automatically discover the intrinsic patterns from the abundant unlabeled data that are usually readily available (from Internet) and require no laborious human labeling. In this dissertation, the author will explore the capability of feature learning algorithms in automated biomedical image analysis. Specifically, the author will present two unsupervised feature learning models for histopathology image classification. The author will also introduce a novel convolutional regression model for nuclei segmentation. Experiments on biomedical image classification and segmentation benchmarks demonstrate that the proposed feature learning systems can achieve very competitive results compared to dedicated systems incorporating biological prior knowledge.

Finally, the author proposes a sparse coding based framework for classifying complicated human gestures represented as multi-variate time series (MTS). Specifically, the author will present a novel feature extraction strategy, which can overcome the problem of inconsistent lengths among MTS data and is robust to the large variability within human gestures. Moreover, the author will introduce a generic approach to kernelize sparse representation, which leads to enhanced classification performance. Extensive experiments verify the effectiveness of the proposed framework.

For details, please visit the thesis page.

Table of Contents:

SPS on Twitter

  • THIS FRIDAY: Join our Vice President-Membership, K.V.S. Hari, and Membership Development Committee Chair, Arash Moh… https://t.co/rGSzhHAwgM
  • The SPACE webinar series continues tomorrow, Tuesday, 11 August at 11 AM ET with Dr. Xiao Xiang Zhu presenting "Dat… https://t.co/X5oz4KiJwX
  • now accepting submissions for special sessions, tutorials, and papers! The conference is set for June 2… https://t.co/sB3o5ItL0j
  • DEADLINE EXTENDED: The IEEE Journal of Selected Topics in Signal Processing is now accepting papers for a Special I… https://t.co/2SJwqj7aDB
  • NEW WEBINAR: Join us on Friday, 14 August at 11:00 AM ET for the 2021 SPS Membership Preview! Society leadership wi… https://t.co/1PLaZIt2VQ

SPS Videos


Signal Processing in Home Assistants

 


Multimedia Forensics


Careers in Signal Processing             

 


Under the Radar