  App classification is useful in a number of applications such as adding apps
to an app store or building a user model based on the installed apps. Presently
there are a number of existing methods to classify apps based on a given
taxonomy on the basis of their text metadata. However, text based methods for
app classification may not work in all cases, such as when the text
descriptions are in a different language, or missing, or inadequate to classify
the app. One solution in such cases is to utilize the app images to supplement
the text description. In this paper, we evaluate a number of approaches in
which app images can be used to classify the apps. In one approach, we use
Optical character recognition (OCR) to extract text from images, which is then
used to supplement the text description of the app. In another, we use pic2vec
to convert the app images into vectors, then train an SVM to classify the
vectors to the correct app label. In another, we use the captionbot.ai tool to
generate natural language descriptions from the app images. Finally, we use a
method to detect and label objects in the app images and use a voting technique
to determine the category of the app based on all the images. We compare the
performance of our image-based techniques to classify a number of apps in our
dataset. We use a text based SVM app classifier as our base and obtained an
improved classification accuracy of 96% for some classes when app images are
added.
