A Large-scale Image-based Bookstore Inventory Check System Using Vocabulary Tree

• Main Authors: Chuang, Minghan, 莊明翰
• Other Authors: Liu, Jenchang
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/05026015902621338638

碩士 === 國立暨南國際大學 === 資訊工程學系 === 100 === Libraries and bookstores both have a large number of book collections, when the collection becomes greater, the number of checkouts and returning books during the operating hours will also become greater, therefore, bookstores tend to spend a lot of time and manpower for large-scale inventory checking at regular intervals. During the period of large-scale inventory checking, the bookstore is not operating, it takes a great cost for the bookstore. This thesis proposes an automatic large-scale image-based bookstore inventory checking system. To capture images automatically, we experimentally use the AR-Drone (a kind of indoor helicopter) to take pictures, and then we implement bookspine segmentation and matching in a small-scale database. In the experiments of large-scale image database, we extract the SIFT (Scale-Invariant Features Transform) features from bookspine images, then use exhaustive search, vocabulary tree, and a hybrid method to conduct image matching experiments. Because exhaustive search is time-consuming, we establish the vocabulary tree by hierarchically K-means cluster those database images’ SIFT features on a Hadoop platform. The SIFT features on the query image and the database images can be quantized into visual word vectors by using the vocabulary tree, and it will reduce the time for image matching. Besides, the thresholds on the matching scores are determined when using vocabulary tree and exhaustive search, which make our system capable to determine whether the query image is in the original database or not. Because K-means clustering would produce a different result at each run, five large-scale experiments were conducted. Book recognition rate achieved 80 percent for exhaustive search, 70 percent for vocabulary tree and 75 percent for the hybrid method. For automatic image capturing using AR-Drone, recognition rate in the small-scale experiment can achieve 60 percent even the quality and resolution of the captured images are low.