Attribute distance for fast classification systems

作者：Wu, Weining^*

来源：Expert Systems with Applications, 2018, 105: 183-195.

DOI：10.1016/j.eswa.2018.03.049

• Classification systems
• Active feedback
• Attribute selection
• Similarity evaluation

Active classification systems developed by machine learning researchers have helped to recognize and retrieve object categories more rapidly. However, a large number of studies have focused on designing feedback procedures or sampling algorithms that maximize the information contained in individual examples for training the classifier, but it results in increasing time expense with the size of database. In this work, an attribute distance is proposed directly on visual attributes. And then, active classification systems are constructed on the chosen examples in a short time. Our contributions are reported as follows: (1) a small set of visual attributes can be discriminative for current classification tasks, and therefore, it can speed up the computation of example-to-hyperplane distances; (2) discriminative attributes are sequentially observed by using their scores given by the classifier, corresponding to target objects. We apply the attribute distance in several applications and show experimental results of speeding up feedback procedures. Moreover, the attribute distance can bring insight into designing a fast sampling strategy which is able to further improve the efficiency for existing classification systems.

• 出版日期2018-9-1
• 单位哈尔滨工程大学