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    題名: A Content-Based Image Retrieval Method Based on the Google Cloud Vision API with WordNet
    作者: Chen, Shih-Hsin;Chen, Yi-Hui
    關鍵詞: Content Based Image Retrieval;Image annotation;Google Cloud Vision API;WordNet;Pascal VOC 2007
    日期: 2017-02-26
    上傳時間: 2021-10-21 12:11:39 (UTC+8)
    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.
    關聯: Intelligent Information and Database Systems, p.651-662
    DOI: 10.1007/978-3-319-54472-4_61
    顯示於類別:[資訊工程學系暨研究所] 會議論文

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