教師資料查詢 | 類別: 會議論文 | 教師: 陳世興CHEN, SHIH-HSIN (瀏覽個人網頁)

標題: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
作品名稱(其他語言)
著者Shih-Hsin Chen; Yi-Hui Chen
作品所屬單位
出版者
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
語言英文
收錄於
會議性質國際
校內研討會地點
研討會時間20170403~20170405
通訊作者
國別日本
公開徵稿
出版型式
出處Intelligent Information and Database Systems, p.651-662
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