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Title:Fine-grained artworks classification
Author(s):Huang, Jing
Advisor(s):Lazebnik, Svetlana
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):Convolutional Neural Networks
Fine-grained Classification
Artworks Classification
Abstract:In this thesis, we apply deep convolutional neural networks to ne-grained artwork classification on the large-scale painting collection, WikiArt. We propose a new architecture that aggregates features from different convolutional layers to exploit earlier layer features. The new architecture is evaluated on the challenging fine-grained artist and year classification. We also propose a regularization method that penalizes correlations of convolutional feature maps. With the decorrelation regularization, we further improve the classification accuracy of the proposed architecture.
Issue Date:2018-04-24
Type:Text
URI:http://hdl.handle.net/2142/101050
Rights Information:Copyright 2018 Jing Huang
Date Available in IDEALS:2018-09-04
Date Deposited:2018-05


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