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Communication Dans Un Congrès Année : 2012

SEMANTIC SEGMENTATION VIA SPARSE CODING OVER HIERARCHICAL REGIONS

Résumé

The purpose of this paper is segmenting objects in an image and assigning a predefined semantic label to each object. There are two areas of novelty in this paper. On one hand, hierarchical regions are used to guide semantic segmenta-tion instead of using single-level regions or multi-scale regions generated by multiple segmentations. On the other hand, sparse coding is introduced as high level description of the regions, which contributes to less quantization error than traditional bag-of-visual-words method. Experiments on the challenging Microsoft Research Cambridge dataset (MSRC 21) show that our algorithm achieves state-of-the-art performance.
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Dates et versions

hal-00728136 , version 1 (04-09-2012)

Identifiants

  • HAL Id : hal-00728136 , version 1

Citer

Wenbin Zou, Kidiyo Kpalma, Joseph Ronsin. SEMANTIC SEGMENTATION VIA SPARSE CODING OVER HIERARCHICAL REGIONS. IEEE International Conference on Image Processing (ICIP), Sep 2012, Orlando, United States. 4 p. ⟨hal-00728136⟩
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