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
Volume
11854
Pagination
404-415
Location
Sydney, N.S.W.
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)
Lee C, Su Z, Sugimoto A
Title of proceedings
Image and video technology : 9th Pacific-Rim Symposium, PSIVT 2019, Sydney, NSW, Australia, November 18-22, 2019, Proceedings
Event
Image and Video Technology. Pacific-Rim Symposium (9th : 2019 : Sydney, N.S.W.)