文章基本信息

标题：Content-Based Image Retrieval using Local Features Descriptors and Bag-of-Visual Words
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作者：Mohammed Alkhawlani ; Mohammed Elmogy ; Hazem Elbakry 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2015
卷号：6
期号：9
DOI：10.14569/IJACSA.2015.060929
出版社：Science and Information Society (SAI)
摘要：Image retrieval is still an active research topic in the computer vision field. There are existing several techniques to retrieve visual data from large databases. Bag-of-Visual Word (BoVW) is a visual feature descriptor that can be used successfully in Content-based Image Retrieval (CBIR) applications. In this paper, we present an image retrieval system that uses local feature descriptors and BoVW model to retrieve efficiently and accurately similar images from standard databases. The proposed system uses SIFT and SURF techniques as local descriptors to produce image signatures that are invariant to rotation and scale. As well as, it uses K-Means as a clustering algorithm to build visual vocabulary for the features descriptors that obtained of local descriptors techniques. To efficiently retrieve much more images relevant to the query, SVM algorithm is used. The performance of the proposed system is evaluated by calculating both precision and recall. The experimental results reveal that this system performs well on two different standard datasets.
关键词：thesai; IJACSA; thesai.org; journal; IJACSA papers; Content-based Image Retrieval (CBIR); Scale Invariant Feature Transform (SIFT); Speeded Up Robust Features (SURF); K-Means Algorithm; Support Vector Machine (SVM); Bag-of-Visual Word (BoVW)