User Constrained Thumbnail Generation Using Adaptive Convolutions
Perla Sai Raj Kishore, Ayan Kumar Bhunia, Shuvozit Ghose, Partha Pratim Roy
Abstract
Thumbnails are widely used all over the world as a preview for digital images. In this work we propose a deep neural framework to generate thumbnails of any size and aspect ratio, even for unseen values during training, with high accuracy and precision. We use Global Context Aggregation (GCA) and a modified Region Proposal Network (RPN) with adaptive convolutions to generate thumbnails in real time. GCA is used to selectively attend and aggregate the global context information from the entire image while the RPN is used to generate candidate bounding boxes for the thumbnail image. Adaptive convolution eliminates the difficulty of generating thumbnails of various aspect ratios by using filter weights dynamically generated from the aspect ratio information. The experimental results indicate the superior performance of the proposed model <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> over existing state-of-the-art techniques.
BibTeX
@inproceedings{icassp2019_userconstrainedt,
title = {User Constrained Thumbnail Generation Using Adaptive Convolutions},
author = {Perla Sai Raj Kishore and Ayan Kumar Bhunia and Shuvozit Ghose and Partha Pratim Roy},
booktitle = {ICASSP 2019},
year = {2019}
}