| Summary: | 碩士 === 崑山科技大學 === 電腦與通訊研究所 === 102 === When the designers want to look for leathers or cloths, the most common way to search the materials is to browse the vendor catalogs. Due to the advance of web technologies, they can use the internet search engines to browse the image database giving either the keywords or pictures of the samples. Sometimes, what the designers thinking in mind is just a style or a concept which is difficult to be described literally and to match the annotation of images in the image database. Using pictures of the samples for image retrieval, or called content-based image retrieval, might be a better way to solve the problems caused by poor literal description.
Current public search engines which support content-based image retrieval functions, such as google or Tineye, retrieve the images mainly based on the similarity of color histograms between the user upload query image and each image in the database. However, for the fabric materials, those with the same texture might appear with different colors. Therefore, merely color feature is not suitable for comparing different fabric materials. In this study, we develop a content-based image retrieval system for leather and fabric materials based on not only the feature of color histogram but also the features derived LBP (local binary pattern), FAST (Features from Accelerated Segment Test) and Haar Wavelet. These features are able to discriminate not only the textures but also the pattern styles. 45 categories of fabrics with various textures, colors and patterns are test in our experiments. Top 9 retrieved images are considered as the candidates. When the search scope is set to ten categories, our system can reach 87.37% retrieval rate. When all 45 categories are test, our system can reach 41.27% retrieval rate.
In the future, we will continue to improve the algorithms to enhance the overall retrieval rate.
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