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Computer Science > Computer Vision and Pattern Recognition

arXiv:2108.01326 (cs)
[Submitted on 3 Aug 2021]

Title:Predicting Popularity of Images Over 30 Days

Authors:Amartya Dutta, Ferdous Ahmed Barbhuiya
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Abstract:The current work deals with the problem of attempting to predict the popularity of images before even being uploaded. This method is specifically focused on Flickr images. Social features of each image as well as that of the user who had uploaded it, have been recorded. The dataset also includes the engagement score of each image which is the ground truth value of the views obtained by each image over a period of 30 days. The work aims to predict the popularity of images on Flickr over a period of 30 days using the social features of the user and the image, as well as the visual features of the images. The method states that the engagement sequence of an image can be said to depend on two independent quantities, namely scale and shape of an image. Once the shape and scale of an image have been predicted, combining them the predicted sequence of an image over 30 days is obtained. The current work follows a previous work done in the same direction, with certain speculations and suggestions of improvement.
Comments: Accepted in ICIP Image Popularity Prediction Challenge 2020, Won 3rd Position in Challenge
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2108.01326 [cs.CV]
  (or arXiv:2108.01326v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2108.01326

Submission history

From: Amartya Dutta [view email]
[v1] Tue, 3 Aug 2021 07:04:27 UTC (417 KB)
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