| Summary: | 碩士 === 逢甲大學 === 資訊電機工程碩士在職專班 === 100 === Content-based images retrieval (CBIR) has been widely used in many application fields. Yet, in commercial photography, ornaments and décor are often used to better the vision of the product as a whole. The images of the digital archive system for Taiwan flower anthography group are usually accompanied by other background objects that have nothing to do with the target plant. The background noise will decrease the precision rate of image retrieval.
Therefore, we propose a method based on Visual Attention Model to extract image area of interest as training datasets, thus improving the retrieval precision rate. The number of digital images is growing rapidly in social networks due to the popularity of the Internet and digital cameras. To deal with the large amount of image data and computation cost, we choose the Self-Organizing Map (SOM) as our unsupervised learning algorithm. As an efficient Artificial Neural Network approach, SOM is useful for visualizing low-dimensional views of high-dimensional data. Our experiments show good performance results.
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