Artist-based classification via deep learning with multi-scale weighted pooling

Kevin Alfianto Jangtjik, Mei Chen Yeh, Kai Lung Hua

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

For analyzing digital images of paintings we propose a new approach to categorize them based on artist. Determining the authorship of a painting is challenging because common subjects are illustrated in paintings, and paintings of an artist may not have a unique style. The proposed approach is built upon convolutional neural networks (CNN)-a class of biologically inspired vision model that recently demonstrates near-human performance on several visual recognition tasks. However, training a CNN model requires large scale training data of a fixed input image size (e.g. 224 × 224). In this paper, we propose to construct a multi-layer pyramid from an image, providing 21X more features than using a single layer (i.e., the original image) alone. We train a CNN model for each layer, and propose a new weighted fusion scheme to adaptively combine the decision results. To evaluate the proposed methods, we collect a new painting image dataset, categorized into 13 artists. As demonstrated in the experimental results, the proposed method achieves a promising result-88.08% recall rate in top-2 retrieval on the challenging classification task.

Original languageEnglish
Title of host publicationMM 2016 - Proceedings of the 2016 ACM Multimedia Conference
PublisherAssociation for Computing Machinery, Inc
Pages635-639
Number of pages5
ISBN (Electronic)9781450336031
DOIs
Publication statusPublished - 2016 Oct 1
Event24th ACM Multimedia Conference, MM 2016 - Amsterdam, United Kingdom
Duration: 2016 Oct 152016 Oct 19

Publication series

NameMM 2016 - Proceedings of the 2016 ACM Multimedia Conference

Other

Other24th ACM Multimedia Conference, MM 2016
CountryUnited Kingdom
CityAmsterdam
Period16/10/1516/10/19

Keywords

  • Convolutional neural network
  • Deep learning
  • Digital painting image classification
  • Spatial pyramid representation

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Software

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  • Cite this

    Jangtjik, K. A., Yeh, M. C., & Hua, K. L. (2016). Artist-based classification via deep learning with multi-scale weighted pooling. In MM 2016 - Proceedings of the 2016 ACM Multimedia Conference (pp. 635-639). (MM 2016 - Proceedings of the 2016 ACM Multimedia Conference). Association for Computing Machinery, Inc. https://doi.org/10.1145/2964284.2967299