Efficient dictionary learning for visual categorization

Jun Tang, Ling Shao, Xuelong Li

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


We propose an efficient method to learn a compact and discriminative dictionary for visual categorization, in which the dictionary learning is formulated as a problem of graph partition. Firstly, an approximate kNN graph is efficiently computed on the data set using a divide-and-conquer strategy. And then the dictionary learning is achieved by seeking a graph topology on the resulting kNN graph that maximizes a submodular objective function. Due to the property of diminishing return and monotonicity of the defined objective function, it can be solved by means of a fast greedy-based optimization. By combing these two efficient ingredients, we finally obtain a genuinely fast algorithm for dictionary learning, which is promising for large-scale datasets. Experimental results demonstrate its encouraging performance over several recently proposed dictionary learning methods.
Original languageEnglish
Pages (from-to)91-98
JournalComputer Vision and Image Understanding
Early online date1 Jun 2014
Publication statusPublished - 1 Jul 2014


  • Visual categorization
  • Efficient dictionary learning
  • Submodular optimization
  • Fast graph construction

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