會議論文
| 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 | en |
| 收錄於 | |
| 會議性質 | 國際 |
| 校內研討會地點 | 無 |
| 研討會時間 | 20170403~20170405 |
| 通訊作者 | |
| 國別 | JPN |
| 公開徵稿 | |
| 出版型式 | |
| 出處 | Intelligent Information and Database Systems, p.651-662 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/121533 ) |