A Gossip-Based System for Fast Approximate Score Computation in Multinomial Bayesian Networks

Zachariah, Arun; Rao, Praveen; Katib, Anas; Senapati, Monica; Barnard, Kobus

doi:10.1109/icde.2019.00216

Cited by 1 publication

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“…His research interest focuses on Knowledge Management, intersecting scalable systems design, data science, and machine learning. He is a part of the Scalable Data Science (SDS) Lab, where he works on designing scalable algorithms and systems for data and knowledge management [102,103,104,105] and information retrieval [106,107,108]. As a part of his Ph.D., he is involved in many interdisciplinary research projects requiring the use of AI and cluster computing technologies to enable gathering insights and solving challenging problems in the energy and healthcare domains, such as sustainable housing design [109], genomic analysis [110,111], cancer cell detection in histopathology images [112,113]…”

Section: Vitamentioning

confidence: 99%

A system for large-scale image and video retrieval on everyday scenes

Zachariah¹

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There has been a growing amount of multimedia data generated on the web todayin terms of size and diversity. This has made accurate content retrieval with these large and complex collections of data a challenging problem. Motivated by the need for systems that can enable scalable and efficient search, we propose QIK (Querying Images Using Contextual Knowledge). QIK leverages advances in deep learning (DL) and natural language processing (NLP) for scene understanding to enable large-scale multimedia retrieval on everyday scenes with common objects. The system consists of three major components: Indexer, Query Processor, and Video Processor. Given an image, the Indexer performs probabilistic image understanding (PIU). The PIU generated consists of the most probable captions, parsed and represented by tree structures using NLP techniques, and detected objects. The PIU's are stored and indexed in a database system. For a query image, the Query Processor generates the most probable caption and parses it into the corresponding tree structure. Then an optimized tree-pattern query is constructed and executed on the database to retrieve a set of candidate images. The candidate images fetched are ranked using the tree-edit distance metric computed on the tree structures. Given a video, the Video Processor extracts a sequence of key scenes that are posed to the Query Processor to retrieve a set of candidate scenes. The candidate scene parse trees corresponding to a video are extracted and are ranked based on the number of matching scenes. We evaluated the performance of our system for large-scale image and video retrieval tasks on datasets containing everyday scenes and observed that our system could outperform state-ofthe- art techniques in terms of mean average precision.

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Section: Vitamentioning

confidence: 99%

A system for large-scale image and video retrieval on everyday scenes

Zachariah¹

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A Gossip-Based System for Fast Approximate Score Computation in Multinomial Bayesian Networks

Cited by 1 publication

References 13 publications

A system for large-scale image and video retrieval on everyday scenes

A system for large-scale image and video retrieval on everyday scenes

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