University of Surrey

Test tubes in the lab Research in the ATI Dance Research

SALIC: Social Active Learning for Image Classification

Chatzilari, E, Nikolopoulos, S, Kompatsiaris, Y and Kittler, JV (2016) SALIC: Social Active Learning for Image Classification IEEE Transactions on Multimedia, 18 (8). pp. 1488-1503.

double.pdf - Accepted version Manuscript
Available under License : See the attached licence file.

Download (3MB) | Preview
Text (licence)
Available under License : See the attached licence file.

Download (33kB) | Preview


In this paper, we present SALIC, an active learning method for selecting the most appropriate user tagged images to expand the training set of a binary classifier. The process of active learning can be fully automated in this social context by replacing the human oracle with the images' tags. However, their noisy nature adds further complexity to the sample selection process since, apart from the images' informativeness (i.e., how much they are expected to inform the classifier if we knew their label), our confidence about their actual label should also be maximized (i.e., how certain the oracle is on the images' true contents). The main contribution of this work is in proposing a probabilistic approach for jointly maximizing the two aforementioned quantities. In the examined noisy context, the oracle's confidence is necessary to provide a contextual-based indication of the images' true contents, while the samples' informativeness is required to reduce the computational complexity and minimize the mistakes of the unreliable oracle. To prove this, first, we show that SALIC allows us to select training data as effectively as typical active learning, without the cost of manual annotation. Finally, we argue that the speed-up achieved when learning actively in this social context (where labels can be obtained without the cost of human annotation) is necessary to cope with the continuously growing requirements of large-scale applications. In this respect, we demonstrate that SALIC requires ten times less training data in order to reach the same performance as a straightforward informativeness-agnostic learning approach.

Item Type: Article
Subjects : Electronic Engineering
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
Chatzilari, E
Nikolopoulos, S
Kompatsiaris, Y
Kittler, JV
Date : August 2016
DOI : 10.1109/TMM.2016.2565440
Copyright Disclaimer : © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Depositing User : Symplectic Elements
Date Deposited : 18 Oct 2016 14:57
Last Modified : 31 Oct 2017 18:48

Actions (login required)

View Item View Item


Downloads per month over past year

Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800