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| November 3
| November 3
| Abhishek Sharma
| Abhishek Sharma
| Cross-modal classification and retrieval : Techniques and challenges
| Cross-modal classification and retrieval: Techniques and challenges
|-
|-
| November 10
| November 10
Visual tracking is a classical computer vision task. However, the ubiquity of modern sensors makes it more difficult due to the large amount of data available for processing. The emerging theory of compressive sensing has the potential to address this problem in that it promises the ability to reduce the amount of data collected without sacrificing the amount of information within. In this talk, I will review recent research research toward the adaptation of some computer vision algorithms commonly used in visual tracking such that they can operate in the lower-dimensional compressive domain. Specifically, background subtraction and particle filtering will be discussed.
Visual tracking is a classical computer vision task. However, the ubiquity of modern sensors makes it more difficult due to the large amount of data available for processing. The emerging theory of compressive sensing has the potential to address this problem in that it promises the ability to reduce the amount of data collected without sacrificing the amount of information within. In this talk, I will review recent research research toward the adaptation of some computer vision algorithms commonly used in visual tracking such that they can operate in the lower-dimensional compressive domain. Specifically, background subtraction and particle filtering will be discussed.
===Cross-modal classification and retrieval : Techniques and challenges===
===Cross-modal classification and retrieval: Techniques and challenges===
Speaker: [http://www.cs.umd.edu/~bhokaal/ Abhishek Sharma] -- Date: November 3, 2011
Speaker: [http://www.cs.umd.edu/~bhokaal/ Abhishek Sharma] -- Date: November 3, 2011
Classification data arrives in multiple forms of representations and distributions (modality) having a common underlying content. Classification or Retrieval is required to be done solely based on the content irrespective of the modality. For example - given a text description of a topic (history) find appropriate images from a database OR given a person's face image in some pose and lighting which is different than that of the gallery, find the matching face OR based on user supplied tags find matching images from the database. These problems are finding applications everywhere because of wide-spread Internet and extremely cheap sensors (cameras and keyboards).
Classification data arrives in multiple forms of representations and distributions (modality) having a common underlying content. Classification or Retrieval is required to be done solely based on the content irrespective of the modality. For example - given a text description of a topic (history) find appropriate images from a database OR given a person's face image in some pose and lighting which is different than that of the gallery, find the matching face OR based on user supplied tags find matching images from the database. These problems are finding applications everywhere because of wide-spread Internet and extremely cheap sensors (cameras and keyboards).
In this talk, we will go over some popular techniques from the literature to tackle the problem of cross-modal classification and retrieval. Specifically, I will be discussing Canonical Correlational Analysis (and variants), Partial Least Square, Bilinear Model (Freeman and Tannenbaum), Tied Factor Analysis, Probabilstic LDA, Multi-view LDA and SVM-2k along with detailed pros and cons of each of these. Then I will present a comparative application of all these approaches along with recent methods for pose and lighting invariant face recognition as a case study.
In this talk, we will go over some popular techniques from the literature to tackle the problem of cross-modal classification and retrieval. Specifically, I will be discussing Canonical Correlational Analysis (and variants), Partial Least Square, Bilinear Model (Freeman and Tannenbaum), Tied Factor Analysis, Probabilstic LDA, Multi-view LDA and SVM-2k along with detailed pros and cons of each of these. Then I will present a comparative application of all these approaches along with recent methods for pose and lighting invariant face recognition as a case study.
