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ICIG
2009
IEEE

A PLSA-Based Semantic Bag Generator with Application to Natural Scene Classification under Multi-instance Multi-label Learning F

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A PLSA-Based Semantic Bag Generator with Application to Natural Scene Classification under Multi-instance Multi-label Learning F
Classifying natural scenes into semantic categories has always been a challenging task. So far, many works in this field are primarily intended for single label classification, where each scene example is represented as a single instance vector. The multi-instance multilabel (MIML) learning framework proposed by Z. H. Zhou et al [1] provides a new solution to the problem of scene classification in a different way. In this paper, we propose a novel scene classification method based on pLSA-based semantic bag generator and MIML learning framework. Under the framework of MIML learning, we introduce the mechanism that transfers an image into a set of instances through the pLSA-based bag generator. Experiments show that our approach achieves better classification performance comparing with the previous work.
Shuangping Huang, Lianwen Jin
Added 23 May 2010
Updated 23 May 2010
Type Conference
Year 2009
Where ICIG
Authors Shuangping Huang, Lianwen Jin
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