A content-based image retrieval method based on the google cloud vision API and wordnet

Shih Hsin Chen; Yi Hui Chen

doi:10.1007/978-3-319-54472-4_61

A content-based image retrieval method based on the google cloud vision API and wordnet

Shih Hsin Chen
, Yi Hui Chen^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

29 Scopus citations

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.

Original language	English
Title of host publication	Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings
Editors	Satoshi Tojo, Le Minh Nguyen, Ngoc Thanh Nguyen, Bogdan Trawinski
Publisher	Springer Verlag
Pages	651-662
Number of pages	12
ISBN (Print)	9783319544717
DOIs	https://doi.org/10.1007/978-3-319-54472-4_61
State	Published - 2017
Externally published	Yes
Event	9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017 - Kanazawa, Japan Duration: 03 04 2017 → 05 04 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10191 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017
Country/Territory	Japan
City	Kanazawa
Period	03/04/17 → 05/04/17

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.

Keywords

Content based image retrieval
Google cloud vision API
Image annotation
Pascal VOC 2007
WordNet

Access to Document

10.1007/978-3-319-54472-4_61

Cite this

Chen, S. H., & Chen, Y. H. (2017). A content-based image retrieval method based on the google cloud vision API and wordnet. In S. Tojo, L. M. Nguyen, N. T. Nguyen, & B. Trawinski (Eds.), Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings (pp. 651-662). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10191 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-54472-4_61

Chen, Shih Hsin ; Chen, Yi Hui. / A content-based image retrieval method based on the google cloud vision API and wordnet. Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings. editor / Satoshi Tojo ; Le Minh Nguyen ; Ngoc Thanh Nguyen ; Bogdan Trawinski. Springer Verlag, 2017. pp. 651-662 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A content-based image retrieval method based on the google cloud vision API and wordnet",

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.",

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author = "Chen, \{Shih Hsin\} and Chen, \{Yi Hui\}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017 ; Conference date: 03-04-2017 Through 05-04-2017",

year = "2017",

doi = "10.1007/978-3-319-54472-4\_61",

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Chen, SH & Chen, YH 2017, A content-based image retrieval method based on the google cloud vision API and wordnet. in S Tojo, LM Nguyen, NT Nguyen & B Trawinski (eds), Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10191 LNAI, Springer Verlag, pp. 651-662, 9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017, Kanazawa, Japan, 03/04/17. https://doi.org/10.1007/978-3-319-54472-4_61

A content-based image retrieval method based on the google cloud vision API and wordnet. / Chen, Shih Hsin; Chen, Yi Hui.
Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings. ed. / Satoshi Tojo; Le Minh Nguyen; Ngoc Thanh Nguyen; Bogdan Trawinski. Springer Verlag, 2017. p. 651-662 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10191 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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N2 - 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.

AB - 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.

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SP - 651

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A2 - Nguyen, Ngoc Thanh

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Chen SH, Chen YH. A content-based image retrieval method based on the google cloud vision API and wordnet. In Tojo S, Nguyen LM, Nguyen NT, Trawinski B, editors, Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings. Springer Verlag. 2017. p. 651-662. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-54472-4_61

A content-based image retrieval method based on the google cloud vision API and wordnet

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Keywords

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