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Patch Relational Covariance Distance Similarity Approach for Image Ranking in Content-Based Image Retrieval

Authors:

Piyavach Khunsongkiet,

Jakramate Bootkrajang,

Churee TechawutAuthors Info & Claims

ICCCM '20: Proceedings of the 8th International Conference on Computer and Communications Management

Pages 38 - 43

https://doi.org/10.1145/3411174.3411200

Published: 26 August 2020 Publication History

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ICCCM '20: Proceedings of the 8th International Conference on Computer and Communications Management

Patch Relational Covariance Distance Similarity Approach for Image Ranking in Content-Based Image Retrieval

Pages 38 - 43

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Abstract

Content-Based Image Retrieval (CBIR) is an information retrieval framework for retrieving similar images based on objects in the images. Machine learning based CBIR consists of object detection, the majority of which rely on Convolutional Neural Network (CNN) as object detector, and image similarity ranking. However, object detection with CNN requires expensive retraining when new set of the images is added to the database, while current ranking techniques focus on visual characteristics without considering object's spatial information.

In this work, we propose a new CBIR framework to alleviate the aforementioned problems. We employ the Hierarchical Deep Convolutional Neural Network (HD-CNN) for single object detection. HD-CNN has been shown to be more efficient in model retraining on partitions of large dataset.

In addition, a new similarity measure based on the covariance descriptor called Patch Relational Covariance Distance Similarity (PRCDS) is proposed. PRCDS summarizes the low-level visual features as well as object's spatial information (patch arrangement descriptor) to rank the candidate images from the HD-CNN.

Finally, the proposed framework was validated on a subset of ImageNet dataset, and the experimental results showed that the ranking based on the newly proposed similarity measure is consistent with human perception.

References

[1]

W. Bian and D. Tao, "Biased Discriminant Euclidean Embedding for Content-Based Image Retrieval," IEEE Trans. Image Process., vol. 19, no. 2, pp. 545--554, Feb. 2010.

Digital Library

Google Scholar

[2]

A. V. Singh, "Content-Based Image Retrieval using Deep Learning."

Google Scholar

[3]

Z. Yan et al., "HD-CNN: Hierarchical Deep Convolutional Neural Network for Large Scale Visual Recognition," Oct. 2014.

Google Scholar

[4]

Ruigang Fu, Biao Li, Yinghui Gao, and Ping Wang, "Content-based image retrieval based on CNN and SVM," in 2016 2nd IEEE International Conference on Computer and Communications (ICCC), 2016, pp. 638--642.

Google Scholar

[5]

C. Yao, Y. Zhang, Y. Zhang, and H. Liu, "APPLICATION OF CONVOLUTIONAL NEURAL NETWORK IN CLASSIFICATION OF HIGH RESOLUTION AGRICULTURAL REMOTE SENSING IMAGES," ISPRS - Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., vol. XLII-2/W7, pp. 989--992, Sep. 2017.

Crossref

Google Scholar

[6]

K. R. Kruthika, Rajeswari, and H. D. Maheshappa, "CBIR system using Capsule Networks and 3D CNN for Alzheimer's disease diagnosis," Informatics Med. Unlocked, Dec. 2018.

Google Scholar

[7]

A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks." pp. 1097--1105, 2012.

Digital Library

Google Scholar

[8]

Y. Liu, D. Zhang, G. Lu, and W.-Y. Ma, "A survey of content-based image retrieval with high-level semantics," Pattern Recognit., vol. 40, no. 1, pp. 262--282, Jan. 2007.

Digital Library

Google Scholar

[9]

O. Tuzel, F. Porikli, and P. Meer, "Region Covariance: A Fast Descriptor for Detection and Classification," in Proceedings of the 9th European conference on Computer Vision - Volume Part II, Springer-Verlag, 2006, pp. 589--600.

Google Scholar

[10]

W. Förstner and B. Moonen, "A Metric for Covariance Matrices," in Geodesy-The Challenge of the 3rd Millennium, Berlin, Heidelberg: Springer Berlin Heidelberg, 2003, pp. 299--309.

Google Scholar

[11]

W. Backhaus, R. Kliegl, and J. S. Werner, Color vision: perspectives from different disciplines. Walter de Gruyter, 1998.

Crossref

Google Scholar

[12]

W. Goo, J. Kim, G. Kim, and S. J. Hwang, "Taxonomy-Regularized Semantic Deep Convolutional Neural Networks."

Google Scholar

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Khunsongkiet PBootkrajang JTechawut C(2023)Low-level feature image retrieval using representative images from minimum spanning tree clusteringMultimedia Tools and Applications10.1007/s11042-023-15605-583:2(3335-3356)Online publication date: 7-Jun-2023
https://dl.acm.org/doi/10.1007/s11042-023-15605-5

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ICCCM '20: Proceedings of the 8th International Conference on Computer and Communications Management

July 2020

152 pages

ISBN:9781450387668

DOI:10.1145/3411174

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Natl University of Singapore: National University of Singapore
SFU: Simon Fraser University
Western Michigan University: Western Michigan University
University of Sydney Australia

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Association for Computing Machinery

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Published: 26 August 2020

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ICCCM'20: 2020 The 8th International Conference on Computer and Communications Management

July 17 - 19, 2020

Singapore, Singapore

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Khunsongkiet PBootkrajang JTechawut C(2023)Low-level feature image retrieval using representative images from minimum spanning tree clusteringMultimedia Tools and Applications10.1007/s11042-023-15605-583:2(3335-3356)Online publication date: 7-Jun-2023
https://dl.acm.org/doi/10.1007/s11042-023-15605-5

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Multi-modal Deep Neural Network application on feature extraction and content-based image Retrieval

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