Filter-invariant image classification on social media photos
Journal
MM 2015 - Proceedings of the 2015 ACM Multimedia Conference
Pages
855-858
ISBN
9781450334594
Date Issued
2015
Author(s)
Abstract
With the popularity of social media nowadays, tons of photos are uploaded everyday. To understand the image content, image classification becomes a very essential technique for plenty of applications (e.g., object detection, image caption generation). Convolutional Neural Network (CNN) has been shown as the state-of-The-Art approach for image classi-fication. However, one of the characteristics in social media photos is that they are often applied with photo filters, especially on Instagram. We find that prior works do not aware of this trend in social media photos and fail on filtered images. Thus, we propose a novel CNN architecture that utilizes the power of pairwise constraint by combining Siamese network and the proposed adaptive margin contrastive loss with our discriminative pair sampling method to solve the problem of filter bias. To the best of our knowledge, this is the first work to tackle filter bias on CNN and achieve stateof-the-Art performance on a filtered subset of ILSVRC2012. ? 2015 ACM.
Subjects
Convolutional Neural Network (CNN); Filter Bias; Image Classification; Photo Filter; Siamese Network
SDGs
Other Subjects
Bandpass filters; Convolution; Neural networks; Social networking (online); Convolutional neural network; Filter Bias; Filtered images; Pairwise constraints; Photo Filter; Sampling method; State-of-the-art approach; State-of-the-art performance; Image classification
Type
conference paper