An Image-based Bookstore Inventory Check System Using Bag-of-Words

• Main Authors: Bin-Yi Chou, 周秉宜
• Other Authors: Jen-Chang Liu
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/47663302759734926239

碩士 === 國立暨南國際大學 === 資訊工程學系 === 98 === Book inventory check in libraries or bookstores is a time-consuming and labor-intensive work because of the enormous number of books, but it is necessary for the managers to keep track of the latest information about the books in stock. In addition, careful planning in advance is key to the quality and speed of the inventory check. Thus, the inventory check period is usually very long such that the managers cannot obtain correct information of book inventory timely. In this thesis, an image-based approach is proposed for automatic book inventory management. Bookshelf images are captured by using a number of PTZ web cameras distributed around the bookshelves. Then, a model-based approach is applied to segment bookshelf images into individual book-spine images in which the SIFT features are extracted. The SIFT features along with other information such as the width, the version, ISBN, and the position of the book are stored in the database. To run the inventory check, the same procedure is applied to extract SIFT features of the query book-spine image, then the most similar book-spine image in the database is found via exhaustive feature matching. However, this exhaustive search method is time-consuming because of the enormous number of database images. The concept of Bag-of-Words (BOF) is used to improve the efficiency of search. Experimental results show that the recognition performance using exhaustive search is about 90% and search time using BOF is about 0.05s for each book.