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VISEN: a video interactive retrieval engine based on semantic network in large video collections

Published: 10 June 2019 Publication History

Abstract

Following technological advances carried out recently, there has been an explosion in the quantity of videos available and their accessibility. This is largely justified by the fall of the prices of acquisition and the increase of the capacity of the memory supports, which made the storage of the large document video in computer system possible. To allow an effective exploitation of the collections, it is necessary to install tools facilitating the access to the documents and handle them. In this context, we propose a multimedia retrieval approach that puts the user at the center of the retrieval process starting from a text query. The new aspects of our proposal is as follows: (i) concerning the indexation part, we propose a new approach allowing a multilevel and semantic classification of videos, (ii) regarding the retrieval part, the inclusion of query expansion mechanism helps the user to formulate the query and the relevance feedback mechanism which helps improve the results considering the user's feedback. Our contribution at the experimental level consists in the implementation of prototype VISEN. In fact the technique proposed have been integrated in system seeks by the contents to evaluate the contribution in terms of effectiveness and precision. After carrying out a set of tests on 2700 videos and 62838 images, the experimental results showed that the proposed algorithm performs well.

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cover image ACM Other conferences
IDEAS '19: Proceedings of the 23rd International Database Applications & Engineering Symposium
June 2019
364 pages
ISBN:9781450362498
DOI:10.1145/3331076
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Published: 10 June 2019

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Author Tags

  1. classification
  2. concept
  3. context
  4. query expansion
  5. relevance feedback
  6. semantic indexing
  7. textual query

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Overall Acceptance Rate 74 of 210 submissions, 35%

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  • (2024)AVR (advancing video retrieval): A new framework guided by multi-level fusion of visual and semantic Features for deep learning-based concept detectionMultimedia Tools and Applications10.1007/s11042-024-20112-2Online publication date: 9-Nov-2024
  • (2023)Efficient text-based query based on multi-level and deep-semantic multimedia indexing and retrievalMultimedia Tools and Applications10.1007/s11042-023-17256-y83:18(55811-55850)Online publication date: 30-Nov-2023
  • (2021)Multimodal Video Indexing (MVI): A New Method Based on Machine Learning and Semi-Automatic Annotation on Large Video CollectionsInternational Journal of Image and Graphics10.1142/S021946782250022X22:02Online publication date: 19-Jun-2021
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  • (2019)Large-Scale Semantic Concept Detection Based On Visual ContentsProceedings of the 17th International Conference on Advances in Mobile Computing & Multimedia10.1145/3365921.3365925(165-174)Online publication date: 2-Dec-2019

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