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App classification is useful in a number of applications such as adding appsto an app store or building a user model based on the installed apps. Presentlythere are a number of existing methods to classify apps based on a giventaxonomy on the basis of their text metadata. However, text based methods forapp classification may not work in all cases, such as when the textdescriptions are in a different language, or missing, or inadequate to classifythe app. One solution in such cases is to utilize the app images to supplementthe text description. In this paper, we evaluate a number of approaches inwhich app images can be used to classify the apps. In one approach, we useOptical character recognition (OCR) to extract text from images, which is thenused to supplement the text description of the app. In another, we use pic2vecto convert the app images into vectors, then train an SVM to classify thevectors to the correct app label. In another, we use the captionbot.ai tool togenerate natural language descriptions from the app images. Finally, we use amethod to detect and label objects in the app images and use a voting techniqueto determine the category of the app based on all the images. We compare theperformance of our image-based techniques to classify a number of apps in ourdataset. We use a text based SVM app classifier as our base and obtained animproved classification accuracy of 96% for some classes when app images areadded.

Evaluating Usage of Images for App Classification