vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff9a90310000004343110001000200 Most of the time, the deep learning classifiers are trained using general-purpose datasets with a lot of classes. Therefore, the performance of these classifiers may not be fruitful. Choosing classifiers and dividing them into groups based on the subjects they cover are possible solutions that could lead to better classifier performance. This makes it clear that a classifier division and selection strategy needs for the proposed optimization to work. With the help of this method, the proposed model for feature extraction can choose an appropriate classifier while taking multimedia constraints into account. There is multimedia with the best values, and the results of using only Stacked Auto Encoder (SAE) classifiers from one domain and ignoring classes from other domains are also given. These are in the same place as the effects of only using classifiers from a certain domain. For high-performance use of SAE-based systems, you need to use a classifier selection technique. This method is also needed for the investigation of multimedia events that need the method. To establish the effectiveness of the multimedia event-based system as well as its dependability, we are making use of traditional evaluation methods such as throughput and accuracy. These measures include the following: When compared to the efficiency of the system when using a classifier with a single class, the efficiency of the system diminishes as the number of classes per classifier increases. This is the case regardless of the other measures. This is the situation about both the throughput and the precision of the operation.
R. Jayadurga1, M. Sathiya2, G.K. Arpana3
Soundarya Institute of Management and Science, India1, Karpagam Institute of Technology, India2, East West College of Engineering, India3
Multimedia Data, Stacked Auto Encoder, Deep Learning, Classifier
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 13 , Issue: 3 , Pages: 2921 - 2927 )
Date of Publication :
Feburay 2023
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307
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