期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2017
卷号：8
期号：1
页码：39-42
语种：English
出版社：Ayushmaan Technologies
摘要：To search real time content from the large scale dataset is a difficult task to do. Thus techniques are required to provide better performance to extract relevant images from the large-scale dataset. Various hashing techniques like MVAGH, CHMIS etc are used to provide search mechanism to search image content from the image dataset. A regularize kernel based nonnegative matrix factorization technique used to map semantic content and provide enhanced searching mechanism. But this technique suffers performance degradation in real time datasets. Thus, a new technique called FPSSA (Frequent Pattern Based semantic and Synaptic Search algorithm) is proposed in this paper. A comparative result analysis of the results is presented. This shows that proposed technique provides enhanced functionality as compare to the existing technique.
