Learning from small data set for object recognition in mobile platforms.

• Main Author: Liu, Siyuan
• Other Authors: Yuan, Xiaohui
• Format: Others
• Language: English
• Published: University of North Texas 2016
• Subjects: object recognition; machine learning; mobile platforms; small data set; feature extraction; Computer vision.; Machine learning.; Mobile computing.; Image processing.
• Online Access: https://digital.library.unt.edu/ark:/67531/metadc849633/

Did you stand at a door with a bunch of keys and tried to find the right one to unlock the door? Did you hold a flower and wonder the name of it? A need of object recognition could rise anytime and any where in our daily lives. With the development of mobile devices object recognition applications become possible to provide immediate assistance. However, performing complex tasks in even the most advanced mobile platforms still faces great challenges due to the limited computing resources and computing power. In this thesis, we present an object recognition system that resides and executes within a mobile device, which can efficiently extract image features and perform learning and classification. To account for the computing constraint, a novel feature extraction method that minimizes the data size and maintains data consistency is proposed. This system leverages principal component analysis method and is able to update the trained classifier when new examples become available . Our system relieves users from creating a lot of examples and makes it user friendly. The experimental results demonstrate that a learning method trained with a very small number of examples can achieve recognition accuracy above 90% in various acquisition conditions. In addition, the system is able to perform learning efficiently.