Automatic Image transformation for Inducing Affect
Current image transformation and recoloring algorithms try to introduce artistic effect in the photographed images, based on users input of target image(s) or selection of pre-designed filters. In this paper we present an automatic image-transformation method that transforms the source image such that it induces an emotional affect on the viewer, as desired by the user. Our method can handle much more diverse set of images than previous methods. A discussion and reasoning of failure cases has been provided, indicating inherent limitation of color-transfer based methods in use of emotion assignment.
Afsheen Rafaqat Ali, Mohsen Ali
British Machine Vision Conference (BMVC) 2017
Using Satellite Imagery for Good: Detecting Communities in Desert and Mapping Vaccination Activities
Deep convolutional neural networks (CNNs) have outperformed existing object recognition and detection algorithms. This paper describes a deep learning approach that analyzes a geo referenced satellite image and efficiently detects built structures in it. A Fully Convolutional Network (FCN) is trained on low-resolution Google earth satellite imagery in order to achieve the end result. The detected built communities are then correlated with the vaccination activity.
Anza Shakeel, Mohsen Ali
High-Level Concepts for Affective Understanding of Images
This paper aims to bridge the affective gap between image content and the emotional response of the viewer, it elicits, by using High-Level Concepts (HLCs). In contrast to previous work that relied solely on low-level features or used convolutional neural network (CNN) as a blackbox, we use HLCs generated by pre-trained CNNs in an explicit way to investigate the relations/associations between these HLCs and a(small)set of Ekmanâ€™s emotional classes. Experimental results have demonstrated that our results are comparable to existing methods, with a clear view of the association between HLCs and emotional classes that is ostensibly missing in most existing work.
Afsheen Rafaqat Ali, Usman Shahid, Mohsen Ali, Jeffrey Ho
Winter Conference on Applications of Computer Vision (WACV) 2017
Deconstructing Binary Classifiers in Computer Vision
This paper develops the novel notion of deconstructive learning and proposes a practical model for deconstructing a broad class of binary classifiers commonly used in vision applications. Specifically, the problem studied in this paper is: Given an image-based binary classifier CC as a black-box oracle, how much can we learn of its internal working by simply querying it? In particular, we demonstrate that it is possible to ascertain the type of kernel function used by the classifier and the number of support vectors using only image queries and ascertain the unknown feature space too.