Synergetic Multi-Semantic Multi-Instance Learning for Scene Recognition

Wen Gang Feng; Xue Chen

doi:10.4028/www.scientific.net/AMM.220-223.2188

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 220-223Synergetic Multi-Semantic Multi-Instance Learning...

Synergetic Multi-Semantic Multi-Instance Learning for Scene Recognition

Abstract:

In this paper, the problem of scene representation is modeled by simultaneously considering the stimulus-driven and instance-related factors in a probabilistic framework. In this framework, a stimulus-driven component simulates the low-level processes in human vision system using semantic constrain; while a instance-related component simulate the high-level processes to bias the competition of the input features. We interpret the synergetic multi-semantic multi-instance learning on five scene database of LabelMe benchmark, and validate scene classification on the fifteen scene database via the SVM inference with comparison to the state-of-arts methods.

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Periodical:

Applied Mechanics and Materials (Volumes 220-223)

Pages:

2188-2191

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.220-223.2188

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Online since:

November 2012

Authors:

Wen Gang Feng, Xue Chen

Keywords:

Multi-Semantic Multi-Instance Learning, Scene Recognition, Synergetic Framework

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