Random Sampling Deep Learning Mechanism for Discovering Unique Property of No Specific Local Feature Images

Abstract

A unique and innovative deep learning mechanism is devised and investigated to discover the underline global property of an image or group of images of no any specific local features. The images of tea, rice, and coffee, etc., are some examples. Further extend and generalize the concept, this paper makes a conjecture that every image or a group of images has its own unique global property which can be used as an ID of that image or group of images. As such, if some properties of the physical product and therefore the property in its corresponding image are altered, one should be able to detect it. This paper makes the first research attempt to address and investigate this issue. Some initial experiments by random sampling deep learning algorithm are devised to explore this conjecture. Based on the devised mechanism, a real time tea authentication application can be built allowing farmers to trustfully establish their own product brand name which is pervasively promoted by enabling customers' real time tea authentication during the purchasing. This real time authentication can be friendly achieved simply by extracting the unique global tea image property captured by customer's mobile phone.