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Large-scale multilabel propagation based on efficient sparse graph construction

Published: 27 December 2013 Publication History

Abstract

With the popularity of photo-sharing websites, the number of web images has exploded into unseen magnitude. Annotating such large-scale data will cost huge amount of human resources and is thus unaffordable. Motivated by this challenging problem, we propose a novel sparse graph based multilabel propagation (SGMP) scheme for super large scale datasets. Both the efficacy and accuracy of the image annotation are further investigated under different graph construction strategies, where Gaussian noise and non-Gaussian sparse noise are simultaneously considered in the formulations of these strategies. Our proposed approach outperforms the state-of-the-art algorithms by focusing on: (1) For large-scale graph construction, a simple yet efficient LSH (Locality Sensitive Hashing)-based sparse graph construction scheme is proposed to speed up the construction. We perform the multilabel propagation on this hashing-based graph construction, which is derived with LSH approach followed by sparse graph construction within the individual hashing buckets; (2) To further improve the accuracy, we propose a novel sparsity induced scalable graph construction scheme, which is based on a general sparse optimization framework. Sparsity essentially implies a very strong prior: for large scale optimization, the values of most variables shall be zeros when the solution reaches the optimum. By utilizing this prior, the solutions of large-scale sparse optimization problems can be derived by solving a series of much smaller scale subproblems; (3) For multilabel propagation, different from the traditional algorithms that propagate over individual label independently, our proposed propagation first encodes the label information of an image as a unit label confidence vector and naturally imposes inter-label constraints and manipulates labels interactively. Then, the entire propagation problem is formulated on the concept of Kullback-Leibler divergence defined on probabilistic distributions, which guides the propagation of the supervision information. Extensive experiments on the benchmark dataset NUS-WIDE with 270k images and its lite version NUS-WIDE-LITE with 56k images well demonstrate the effectiveness and scalability of the proposed multi-label propagation scheme.

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      cover image ACM Transactions on Multimedia Computing, Communications, and Applications
      ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 10, Issue 1
      December 2013
      166 pages
      ISSN:1551-6857
      EISSN:1551-6865
      DOI:10.1145/2559928
      Issue’s Table of Contents
      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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      Publication History

      Published: 27 December 2013
      Accepted: 01 April 2013
      Revised: 01 February 2012
      Received: 01 July 2011
      Published in TOMM Volume 10, Issue 1

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      Author Tags

      1. Image annotation
      2. collaborative multilabel propagation
      3. sparsity induced graph construction

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