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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.
===Saliency-Assisted Navigation of Very Large Landscape Images===
Speaker: [http://www.cs.umd.edu/~ipcy/ Cheuk Yiu Ip] -- Date: November 10, 2011
This work presents the first steps towards navigation of very large images, particularly landscape images, from an interactive visualization perspective. The grand challenge in navigation of very large images is identifying regions of potential interest. We outline a three-step approach. We show that our approach of progressive elicitation is fast and allows rapid identification of regions of interest. Our approach is scalable and computationally reasonable on very large images. We validate the results of our approach by comparing them to user-tagged regions of interest on several very large landscape images from the Internet.
