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PicHunt: Social Media Image Retrieval for Improved Law Enforcement

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 10046)

Abstract

First responders are increasingly using social media to identify and reduce crime for well-being and safety of the society. Images shared on social media hurting religious, political, communal and other sentiments of people, often instigate violence and create law & order situations in society. This results in the need for first responders to inspect the spread of such images and users propagating them on social media. In this paper, we present a comparison between different hand-crafted features and a Convolutional Neural Network (CNN) model to retrieve similar images, which outperforms state-of-art hand-crafted features. We propose an Open-Source-Intelligent (OSINT) real-time image search system, robust to retrieve modified images that allows first responders to analyze the current spread of images, sentiments floating and details of users propagating such content. The system also aids officials to save time of manually analyzing the content by reducing the search space on an average by 67 %.

Keywords

  • Image Retrieval
  • Similar Image
  • Online Social Network
  • Sentiment Analysis
  • Color Histogram

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Notes

  1. 1.

    http://precog.iiitd.edu.in/resources.html

  2. 2.

    https://dev.twitter.com/rest/public/search

  3. 3.

    http://help.sentiment140.com/home.

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Correspondence to Sonal Goel .

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Goel, S., Sachdeva, N., Kumaraguru, P., Subramanyam, A.V., Gupta, D. (2016). PicHunt: Social Media Image Retrieval for Improved Law Enforcement. In: Spiro, E., Ahn, YY. (eds) Social Informatics. SocInfo 2016. Lecture Notes in Computer Science(), vol 10046. Springer, Cham. https://doi.org/10.1007/978-3-319-47880-7_13

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  • DOI: https://doi.org/10.1007/978-3-319-47880-7_13

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