An Accurate Efficient and Scalable Event Based Video Search Method Using Spectral Clustering

2018 ◽  
Vol 15 (2) ◽  
pp. 537-541
Author(s):  
R. G. Sakthivelan ◽  
P. Rajendran ◽  
M. Thangavel

Web mining discovers enormous set of data and gets hidden and valuable information which contains text, images, audio and video files from the web search engine which is software that provides a significant result of information. Video rehabilitation for the context gives efficient comprehension of the video content. Video retrieval refers to the task of retrieving most relevant videos from the video Search engine but the outcome listed result could not achieve the relevant videos according to the user needs. This paper addresses Event based Video Retrieval (EBVR) uses metadata, which gives the accurate result. The aim is detect the circumstances of a focal point such as birthday party. In order to overcome this issue, we proposed a personalization approach which captures the user query relevance to their event. Video preprocessing method used to extract related precision data and spectral clustering technique for Video Categorization which yields event extraction and contributes associated video.

Author(s):  
Zikai Song ◽  
Junqing Yu ◽  
Hengyou Cai ◽  
Yangliu Hu ◽  
Yi-Ping Phoebe Chen

2017 ◽  
Vol 9 (2) ◽  
pp. 1209-1217
Author(s):  
Tarun Sharma ◽  
Mr.Gopinath M.P
Keyword(s):  

2016 ◽  
Vol 6 (2) ◽  
pp. 41-65 ◽  
Author(s):  
Sheetal A. Takale ◽  
Prakash J. Kulkarni ◽  
Sahil K. Shah

Information available on the internet is huge, diverse and dynamic. Current Search Engine is doing the task of intelligent help to the users of the internet. For a query, it provides a listing of best matching or relevant web pages. However, information for the query is often spread across multiple pages which are returned by the search engine. This degrades the quality of search results. So, the search engines are drowning in information, but starving for knowledge. Here, we present a query focused extractive summarization of search engine results. We propose a two level summarization process: identification of relevant theme clusters, and selection of top ranking sentences to form summarized result for user query. A new approach to semantic similarity computation using semantic roles and semantic meaning is proposed. Document clustering is effectively achieved by application of MDL principle and sentence clustering and ranking is done by using SNMF. Experiments conducted demonstrate the effectiveness of system in semantic text understanding, document clustering and summarization.


Author(s):  
Ji-Rong Wen

Web query log is a type of file keeping track of the activities of the users who are utilizing a search engine. Compared to traditional information retrieval setting in which documents are the only information source available, query logs are an additional information source in the Web search setting. Based on query logs, a set of Web mining techniques, such as log-based query clustering, log-based query expansion, collaborative filtering and personalized search, could be employed to improve the performance of Web search.


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