Instagram Filter Recommendation System
Date Issued
2016
Date
2016
Author(s)
Chen, Po-Kuang
Abstract
With the growing use of social photo-sharing apps such as Instagram, more and more filters are provided for users to create different effects of their photos. However, taking Instagram as an example, it has more than 30 filters, making it quite difficult for users to go through all of them and make a final decision. Therefore, we aim to provide a filter recommendation system for users with the help of scene labeling. This system can automatically detect and understand the content of an image, and list top 5 recommend filters to the users, not only saving their time, but also make the user experience more pleasant. Besides, we also provide a novel way to improve vignetting effect. Instead of generally assume the focus of an image being at the center, we use saliency detection to find out where the focus really is, and highlight it with our modified vignette, helping users spot that object more easily.
Subjects
Recommendation system
Scene labeling
Saliency detection
Vignette
Filter
Type
thesis
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