Categorization of spam images and identification of controversial images on mobile phones using machine learning and predictive learning

Abstract

The surge in popularity of instant messaging apps has greatly increased the volume of media exchanges, majorly consisting of images. This has resulted in cluttering of images, low storage, etc. In this paper, we are addressing these issues by proposing a system that combines various techniques of facial recognition, text extraction and Natural language processing and refines them through novel approaches to mark images as important or spam according to user’s behaviour and preferences. Our system classifies the images on user’s smartphone into separate categories and processes them further accordingly. Apart from the prominent categories based on the usability of the image, the system also recognizes duplicate and similar images which facilitates their easy deletion.

Keywords:

• Facial recognition
• Image classification
• Machine leaning
• spam detection
• Computer vision