slugA Semantic Concept Detection for Video Based on Regularized Extreme Learning Machine
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2014, Vol 2, Issue 4
Abstract
This paper explains the concept detection based on the proposed method regularized extreme learning machine. In regularized extreme learning machine deals with the modification of the extreme learning machine to solve the missing data problems. Semantic concept detection is an important step in concept- based semantic video retrieval, which can be regarded as an intermediate descriptor to bridge the semantic gap. Support Vector Machines (SVM) and ELM (Extreme Learning Machine) is most existing methods. However, there are several drawbacks of using SVM, such as the high computational cost and large number of parameters to be optimized. The drawback facing by using ELM is some parameters are needed to be tuned manually. This consumes time for classification process. Instead of these disadvantages we use a proposed ELM called Regularized extreme learning machine (RELM) is used to detect semantic concept of videos. It uses a cascade of L1 penalty (LARS) and L2 penalty (Tikhonov regularization) on ELM (TROP-ELM) to regularize the matrix computation.
Authors and Affiliations
Julin Rose Jacob
Keywords
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- EP ID EP17832
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