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Organizing multimedia data socially

Published: 07 July 2008 Publication History

Abstract

Social network sites such as Orkut, MySpace, Facebook, Flickr, and YouTube are flourishing. On these sites, users create communities, upload/share user generated content such as blogs, photos and videos, and interact with each other directly or indirectly. For analyzing and organizing multimedia data, social networks provide useful signals additional to traditional perceptual signals such as color, texture, shape, and motion. In this talk, I will first explain what some social signals can be useful. I will then present algorithms that can fuse these social signals with text and perceptual features. To deal with a large amount of data and rapidly growing social networks, we have recently developed a number of parallel, online algorithms. I will present parallel implementation of Support Vector Machines, PF-Growth (for association mining), spectral clustering algorithm (including SVD and k-mean), Latent Dirichlet Allocation (LDA), and combinational collaborative filtering.

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      cover image ACM Conferences
      CIVR '08: Proceedings of the 2008 international conference on Content-based image and video retrieval
      July 2008
      674 pages
      ISBN:9781605580708
      DOI:10.1145/1386352
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      New York, NY, United States

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      Published: 07 July 2008

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      Author Tags

      1. large-scale graph mining
      2. multimedia information retrieval
      3. social networks

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