| 英文摘要 | Insect recognition technology has many potential applications such as animal and plant quarantine, agriculture and forestry pests and disease prevention, and ecological system research and protection. However, there is a huge gap between the great demand and the lack of professional entomologists, which becomes a bottleneck of agriculture, forestry, ecology, environmental protection of our country. With the rapid development of computer science and technology, there are significant breakthroughs in computational capabilities, digital image processing technology, computer vision, machine learning, and pattern recognition.Identifying insects using the technology of digital image processing and pattern recognition can compensate for the shortage of entomologists and create enormous economic value for agriculture, forestry, ecology, environmental protection and other industries of our country. To develop automatic insect recognition technology based on computer vision and pattern recognition has significant value from both the academic and application view. With the aim of automatic insect identification, this thesis does a thorough research on image feature extraction, coding method, pooling method and classification of insect images. The main contributions are summarized as follows, 1. We propose a feature level fusion method for insect image recognition. Unlike traditional methods that mostly fuse holistic features, the proposed method fuses three types of local features (local texture, color and shape) before dictionary learning, coding and classification, This can better utilize the discrimination abilities of local features and promote the recognition performance. 2. We propose a discriminative dictionary generation and coding method, and combine with the traditional sparse coding, local soft coding and salient coding. This can make full use of the information of training set, to improve the representation ability of dictionary without increasing its size to increase the insect image recognition accuracy. 3. We propose a hybrid pooling strategy for feature representation. It solves both the problem of the lack of priority issues in sum-pooling and the problem of the ignorance of sub-salient features in max-pooling. This makes better use of the local features to characterize the whole images of insects. 4. We propose a decision fusion method for classifiers of heterogeneous class sets. Practically, we can get images of different parts of insects. For... |
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