A Content-Based Image Retrieval Method Based on the Google Cloud Vision API with WordNet
學年 105
學期 2
發表日期 2017-04-03
作品名稱 A Content-Based Image Retrieval Method Based on the Google Cloud Vision API with WordNet
著者 Shih-Hsin Chen; Yi-Hui Chen
會議名稱 9th Asian Conference on Intelligent Information and Database Systems
會議地點 Kanazawa, Japan
摘要 Content-Based Image Retrieval (CBIR) method analyzes the content of an image and extracts the features to describe images, also called the image annotations (or called image labels). A machine learning (ML) algorithm is commonly used to get the annotations, but it is a time-consuming process. In addition, the semantic gap is another problem in image labeling. To overcome the first difficulty, Google Cloud Vision API is a solution because it can save much computational time. To resolve the second problem, a transformation method is defined for mapping the undefined terms by using the WordNet. In the experiments, a well-known dataset, Pascal VOC 2007, with 4952 testing figures is used and the Cloud Vision API on image labeling implemented by R language, called Cloud Vision API. At most ten labels of each image if the scores are over 50. Moreover, we compare the Cloud Vision API with well-known ML algorithms. This work found this API yield 42.4% mean average precision (mAP) among the 4,952 images. Our proposed approach is better than three well-known ML algorithms. Hence, this work could be extended to test other image datasets and as a benchmark method while evaluating the performances.
關鍵字 Content Based Image Retrieval;Image annotation;Google Cloud Vision API;WordNet;Pascal VOC 2007
語言 en
會議性質 國際
研討會時間 20170403~20170405
國別 JPN
出處 Intelligent Information and Database Systems, p.651-662

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/121533 )