Open AccessRecognition improvement through the optimisation of learning instances
Author(s) -
Wu Hao,
Miao Zhenjiang,
Chen Jingyue,
Yang Jie,
Gao Xing
Publication year - 2015
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2014.0094
Subject(s) - computer science , artificial intelligence , wordnet , machine learning , field (mathematics) , process (computing) , pattern recognition (psychology) , image (mathematics) , feature (linguistics) , k nearest neighbors algorithm , selection (genetic algorithm) , feature selection , mathematics , linguistics , philosophy , pure mathematics , operating system
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.