| Summary: | 碩士 === 國立高雄第一科技大學 === 資訊管理研究所 === 101 === Due to the rapid development of information technology and upgrade retrieval technology, Content-Based Image Retrieval has become a popular technique in image search application. The generally used features for image indexing include shape, texture and color. However, these low-level features would not work very well when they are applied in practical applications. In this study, we focus on the seashell image recognition topic. The existed seashell indexing websites can only provide some traditional search modes such as keyword indexing which lack of humanization. This will make the users feel bad in usage if they lack the professional background of seashell.
We develop a two-pass recognition system using CCD shape feature and SURF features to recognize seashell images. In order to increase the efficiency of feature calculation in the system, user can input a front and back seashell image through a webcam then the system will output the corresponding information of the query seashell. The used image processing techniques include image noise removal and normalization. We extract the input shape features of seashell images using CCD with 72 distance values. These features are used to compare the pre-defined image features in database image to find a candidate seashell image. If the candidate is located in the uncertain cluster, then a second stage of SURF feature matching is performed to find a proper solution.
Experimental results show that the proposed method has high accuracy and efficiency, the accuracy rate for the front seashell images using the proposed method is 96% and the average execution time is 0.59 seconds; the accuracy rate for the back seashell images is 100% and the average execution time is 0.66 seconds.
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