Face Indexing and Retrieval by Spatial Similarity

2008 ◽  
Author(s):  
Vikram T. N. ◽  
Chidananda Gowda K. ◽  
Guru D. S. ◽  
Shalini R. Urs
Keyword(s):  
Spatial Similarity ◽  
Indexing And Retrieval ◽  
Face Indexing

Feature Normalization for Video Indexing and Retrieval

1996 ◽  
Author(s):  
Vikrant Kobla ◽  
David Doermann ◽  
King-Ip Lin ◽  
Christos Faloutsos
Keyword(s):  
Video Indexing ◽  
Indexing And Retrieval ◽  
Feature Normalization ◽  
Video Indexing And Retrieval

Multimedia indexing and retrieval

1998 ◽  
Vol 32 (2) ◽  
pp. 29-30
Author(s):  
Rohini K. Srihari ◽  
Zhongfei Zhang ◽  
R. Manmatha ◽  
Chandu Ravela
Keyword(s):  
Multimedia Indexing ◽  
Indexing And Retrieval

Efficient indexing and retrieval of patient information from the big data using MapReduce framework and optimisation

2021 ◽  
pp. 016555152110137
Author(s):  
N.R. Gladiss Merlin ◽  
Vigilson Prem. M
Keyword(s):  
Big Data ◽  
Similarity Measure ◽  
Patient Information ◽  
Complex Data ◽  
Mapreduce Framework ◽  
Maximum Value ◽  
User Query ◽  
Indexing And Retrieval ◽  
Sine Cosine Algorithm ◽  
Disparate Source

Large and complex data becomes a valuable resource in biomedical discovery, which is highly facilitated to increase the scientific resources for retrieving the helpful information. However, indexing and retrieving the patient information from the disparate source of big data is challenging in biomedical research. Indexing and retrieving the patient information from big data is performed using the MapReduce framework. In this research, the indexing and retrieval of information are performed using the proposed Jaya-Sine Cosine Algorithm (Jaya–SCA)-based MapReduce framework. Initially, the input big data is forwarded to the mapper randomly. The average of each mapper data is calculated, and these data are forwarded to the reducer, where the representative data are stored. For each user query, the input query is matched with the reducer, and thereby, it switches over to the mapper for retrieving the matched best result. The bilevel matching is performed while retrieving the data from the mapper based on the distance between the query. The similarity measure is computed based on the parametric-enabled similarity measure (PESM), cosine similarity and the proposed Jaya–SCA, which is the integration of the Jaya algorithm and the SCA. Moreover, the proposed Jaya–SCA algorithm attained the maximum value of F-measure, recall and precision of 0.5323, 0.4400 and 0.6867, respectively, using the StatLog Heart Disease dataset.

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