Abstract
Detecting irregularity in an image or video is an important task in quality control or automatic visual inspection. This paper presents an image embedding technique for detecting an irregularity or abnormality in images. This can further be utilized in image screening application. In the proposed architecture, deep adversarial autoencoder is trained to extract the features from images. Using these features and skip-gram model, we develop the image2vec architecture to capture contextual probability in an image. Various score aggregation techniques are explored and its performance is reported. As a case study, we present a scenario of foreign body object detection in clinical-grade X-ray images. The proposed approach is found to correctly detect and localize abnormality in images.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
C implementation available at: https://code.google.com/archive/p/word2vec/, and Lua at https://github.com/yoonkim/word2vec_torch, respectively.
References
Correct nonuniform illumination and analyze foreground objects. https://in.mathworks.com/help/images/correcting-nonuniform-illumination.html;jsessionid=3ccd5aed8bc168e712e7558b041c. Accessed 2018
Swallowed coin. https://www.topsimages.com/images/swallowed-coin-02.html. Accessed Sept 2018
Aggarwal, C.C.: Outlier Analysis. Springer Science & Business Media (2013)
Boiman, O., Irani, M.: Detecting irregularities in images and in video. Int. J. Comput. Vis. 74(1), 17–31 (2007)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 15 (2009)
Einarsdóttir, H., Emerson, M.J., Clemmensen, L.H., Scherer, K., Willer, K., Bech, M., Larsen, R., Ersbøll, B.K., Pfeiffer, F.: Novelty detection of foreign objects in food using multi-modal X-ray imaging. Food Control 67, 39–47 (2016)
Goldberg, Y., Levy, O.: word2vec explained: deriving Mikolov et al.’s negative-sampling word-embedding method (2014). arXiv:1402.3722
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)
Javan-Roshtkhari, M.: Visual event description in videos. Ph.D. thesis, McGill University (2014)
Jones, D.J., Bickle, D.I.: Ingested foreign bodies in children. https://radiopaedia.org/. Accessed Sept 2018
Kemp, C.: Be on the lookout for subtle signs of foreignbody ingestion. https://www.aappublications.org. Accessed 2018
Li, W., Mahadevan, V., Vasconcelos, N.: Anomaly detection and localization in crowded scenes. IEEE Trans. Pattern Anal. Mach. Intell. 36(1), 18–32 (2014)
Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., Frey, B.: Adversarial autoencoders (2015). arXiv:1511.05644
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Sakhavar, N., Teimoori, B., Ghasemi, M.: Foreign body in the vagina of a four-year-old-girl: a childish prank or sexual abuse. Int. J. High Risk Behav. Addict. 3(2) (2014)
Sterling, J.: Straight, no chaser: when foreign bodies are ingested. https://www.jeffreysterlingmd.com/tag/surgery/. Accessed 2018
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sharma, M.K., Sheet, D., Biswas, P.K. (2020). Image Embedding for Detecting Irregularity. In: Chaudhuri, B., Nakagawa, M., Khanna, P., Kumar, S. (eds) Proceedings of 3rd International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1024. Springer, Singapore. https://doi.org/10.1007/978-981-32-9291-8_20
Download citation
DOI: https://doi.org/10.1007/978-981-32-9291-8_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-32-9290-1
Online ISBN: 978-981-32-9291-8
eBook Packages: EngineeringEngineering (R0)