Mobile phones are used ubiquitously to capture all kinds of images – food, travel, friends, family, receipts, documents, grocery products and many more. Often times when looking back on photos to relive memories, we want to see images that actually represent experiences and not quick convenience photos that were taken for note-keeping and not deleted. Thus, we need to have a solution that presents only the relevant pictures without showing images of receipts, grocery products etc. – termed in general as utility images. This is in the context of a photobook which compiles and shows relevant images from the photo album of a mobile device. Further, all this has to be done on a mobile device since all the media resides there – introducing the need for our system to work on low power devices. In this paper, we present a work that can distinguish between utility and non-utility images. We also present a dataset of utility images and non-utility images with images for each category mentioned. Furthermore, we present a comparison between accuracies of popular pre-trained neural networks and show the trade-off between size and accuracy.
Shopping is difficult for people with motor impairments. This includes online shopping. Proprietary software can emulate mouse and keyboard via head tracking. However, such a solution is not common for smartphones. Unlike desktop and laptop computers, they are also much easier to carry indoors and outdoors. To address this, we implement and open source button that is sensitive to head movements tracked from the front camera of iPhone X. This allows developers to integrate in eCommerce applications easily without requiring specialized knowledge. Other applications include gaming and use in hands-free situations such as during cooking, auto-repair. We built a sample online shopping application that allows users to easily browse between items from various categories and take relevant action just by head movements. We present results of user studies on this sample application and also include sensitivity studies based on two independent tests performed at 3 different distances to the screen.