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Unsupervised deep features for privacy image classification

conference contribution
posted on 2019-01-01, 00:00 authored by Chiranjibi Sitaula, Yong XiangYong Xiang, Sunil AryalSunil Aryal, Xuequan LuXuequan Lu
Sharing images online poses security threats to a wide range of users due to the unawareness of privacy information. Deep features have been demonstrated to be a powerful representation for images. However, deep features usually suffer from the issues of a large size and requiring a huge amount of data for fine-tuning. In contrast to normal images (e.g., scene images), privacy images are often limited because of sensitive information. In this paper, we propose a novel approach that can work on limited data and generate deep features of smaller size. For training images, we first extract the initial deep features from the pre-trained model and then employ the K-means clustering algorithm to learn the centroids of these initial deep features. We use the learned centroids from training features to extract the final features for each testing image and encode our final features with the triangle encoding. To improve the discriminability of the features, we further perform the fusion of two proposed unsupervised deep features obtained from different layers. Experimental results show that the proposed features outperform state-of-the-art deep features, in terms of both classification accuracy and testing time.

History

Event

Image and Video Technology. Pacific-Rim Symposium (9th : 2019 : Sydney, N.S.W.)

Volume

11854

Series

Lecture Notes in Computer Science

Pagination

404 - 415

Publisher

Springer

Location

Sydney, N.S.W.

Place of publication

Cham, Switzerland

Start date

2019-11-18

End date

2019-11-22

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783030348786

ISBN-10

3030348792

Language

eng

Publication classification

E1 Full written paper - refereed

Editor/Contributor(s)

C Lee, Z Su, A Sugimoto

Title of proceedings

Image and video technology : 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18-22, 2019, Proceedings