A Content-Based Image Retrieval Method Based on the Google Cloud Vision API

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Title:	A Content-Based Image Retrieval Method Based on the Google Cloud Vision API
Authors:	陳世興;Shih-Hsin,Chen;陳宜惠;CHEN,YI-HUI
Contributors:	行動商務與多媒體應用學系
Date:	2017
Issue Date:	2017-11-27 03:47:34 (UTC+0)
Abstract:	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.
Relation:	Lecture Notes in Computer Science
Appears in Collections:	[Department of Applied Informatics and Multimedia] Journal Article

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