Open AccessApplying metric space and pivot-based indexing on combined features of bio-images for fast execution of composite queries
Author(s) -
Meenakshi Srivastava,
Sanjay Kumar Singh,
Syed Qamar Abbas
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i1.9009
Subject(s) - search engine indexing , metric (unit) , computer science , feature vector , similarity (geometry) , metric space , construct (python library) , feature (linguistics) , index (typography) , space (punctuation) , database index , component (thermodynamics) , information retrieval , data mining , image (mathematics) , pattern recognition (psychology) , artificial intelligence , mathematics , world wide web , engineering , discrete mathematics , operations management , operating system , linguistics , philosophy , thermodynamics , physics , programming language
Content based recovery of bio images requires index structures, which can retrieve the similar image objects in time proficient way. Conventional Structure/ sequence based recovery of bio-images (for example, protein structures) experiences, tedious online similarity check from huge web based databases. The general approach of image feature representations follows vector based portrayal. In present manuscript, visual highlights of 3D protein structures and their content highlights have been implemented in isolated metric space, rather than vector space which advances the similarity recovery. At long last, the Visual highlights and Content based highlights are consolidated in one metric space, through the component results of highlight and substance metric. Results have demonstrated that pivot based ordering/ indexing on Combined Index Metric can undoubtedly execute composite content construct queries with respect to bio images in time effective way.