Recognition improvement through the optimisation of learning instances. Issue 3 (1st June 2015)

• Record Type: Journal Article
• Authors: Wu, Hao; Miao, Zhenjiang; Chen, Jingyue; Yang, Jie; Gao, Xing
• Is Part Of: IET computer vision. Volume 9:Issue 3(2015)
• Page Start: 419
• Page End: 427
• Publication Date: 2015-06-01
• Subjects: image recognition -- learning (artificial intelligence) -- computer vision; learning instance optimisation -- image recognition -- machine learning -- computer vision fleld -- positive instance -- negative instance -- Wordnet subtree -- semantic distance -- K-nearest neighbour model; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2014.0094
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : Image recognition based on machine learning has been widely utilised in the computer vision field. In the image recognition process, quite a few positive and negative instances are needed for effective machine learning. However, some invalid instances selected from the instance candidates, particularly for negative instances, will result in reduced image recognition accuracy and wasted resources. When making the instance selections for machine learning, if a large number of negative instances are selected that exhibit a semantic distance too close to the positive instances, the recognition accuracy will be considerably reduced. The selection of valid negative instances from the instance candidates has become a new challenge in the field of computer vision. In this paper a new method containing several different algorithms is presented, that more effectively selects valid negative instances. In this innovative process, a Wordnet subtree, semantic distance based on feature descriptors and an improved K‐nearest neighbour model were all utilised. Experiments were implemented using a large database to determine the effectiveness of this method. The results have demonstrated that this method can significantly improve the recognition results.