| Summary: | 碩士 === 國立暨南國際大學 === 資訊工程學系 === 99 === Popularity of the Internet has dramatically changed our life. People are used to ask question on Internet when they have problems. For example of butterfly recognition, we are not limited to querying butterfly field guides or asking the experts. Search engines provide keyword search. However, it is hard to represent butterfly images using proper keywords.
The thesis studies content-based butterfly image recognition. Features are extracted from the natural images containing a butterfly. Three feature extraction methods are compared, including color histogram, SIFT, and semi-local affine part. Similar images in the database are ranked by feature matching. Category of the butterfly can be decided from the search result. The objectness measure is also studied to select the most likely window containing the object in the natural image. In the experiments, the dataset is composed of butterfly images from the Internet, which are not manually segmented and include cluttered background. The best recognition rate is 95.19%.
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