Abstract
This paper presents an algorithmic model for the retrieval of natural flower videos using query by frame mechanism. To overcome the drawback of traditional algorithms, we propose an automated system using a deep convolutional neural network as a feature extractor for the retrieval of videos of flowers. Initially, each flower video is represented by a set of keyframes, then features are extracted from keyframes. For a given query frame, the system extracts deep features and retrieves similar videos from the database using k-nearest neighbor and multiclass support vector machine classifiers. Experiments have been conducted on our own dataset consisting of 1919 videos of flowers belonging to 20 different species of flowers. It can be observed that the proposed system outperforms the traditional flower video retrieval system.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Yu CC, Jou FD, Lee CC, Fan KC, Chuang TC (2008) Efficient multi-resolution histogram matching for fast image/video retrieval. Pattern Recogn Lett 29:1858–1867
Ling B, **ao W, Liu X (2012) Design of video retrieval system using MPEG-7 descriptors. Proc Eng 29:2578–2582
Cui M, Cui J, Li H (2016) Dimensionality reduction for histogram features: a distance-adaptive approach. Neurocomputing 173:181–195
Asha S, Sreeraj M (2013) Content based video retrieval using SURF descriptor. In: Third international conf. on advances in computing and communications, pp 212–215. IEEE Xplore, ISSN:978-0-7695-5033-6
Guru DS, Jyothi VK, Kumar YHS (2019) Features fusion for retrieval of flower videos. Springer Nature. Lecture Notes in Networks and Systems, vol 43, pp 221-233
Zhu Y, Xuang X, Huang Q, Tian Q (2016) Large-scale video copy retrieval with temporal concentration SIFT. Neurocomputing 187:83–91
Mygdalis V, losifidis A, Tefas A, Pitas I (2018) Semi-supervised subclass support vector data description for image and video classification. Neurocomputing 278:51–61
Joe YHN, Matthew H, Sudheendra V (2015) Beyond short snippets: deep networks for video classification. IEEE Xplore, ISSN: 4694-4702
**aohong WG, Rui H, Zengmin T (2017) Classification of CT brain images based on deep learning networks. Comput Methods Programs Biomed 138:49–56
Carlos A, Rossi ALD, Vieira FHA, Andre C Deep learning for biological image classification. Expert systems with applications. 85:114–122
**ngcheng L, Ruihan S, Jian H, Jianhua D, Linji H, Qing G A deep convolutional neural network model for vehicle recognition and face recognition. Proc Comput Sci 107:715–720
Ming HC, Kao SH, Jyh HJ, Nai WL (2013) Classification based video super resolution using artificial neural networks. Sig Process 93:2612–2625
Adnan Q, Anwar SM, Muhammad A, Muhammad M (2017) Medical image retrieval using deep convolutional neural network. Neurocomputing 266:8–20
**nggang W, **ong D, **ang B (2016) Deep sketch feature for cross-domain image retrieval. Neurocomputing 207:387–397
Das M, Manmatha R, Riseman EM (1999) Indexing flower patent images using domain knowledge. IEEE Intell Syst 14:24–33
Jyothi VK, Guru DS, Kumar YHS (2018) Deep learning for retrieval of natural flower videos. Proc Comput Sci 132:1533–1542
Guru DS, Jyothi VK, Kumar YHS (2017) Cluster based approaches for keyframe selection in natural flower videos. Intell Syst Des Appl 736:474–484
Ejaz N, Tariq TB, Baik SW (2012) Adaptive key frame extraction for video summarization using an aggregation mechanism. J Vis Com Image Rep 23(7):1031–1040
Yoshua B (2009) Learning deep architectures for AI. Found Trends Mach Learn 2(1):1–127. https://doi.org/10.1561/2200000006
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. International conference on neural information processing system, vol 1, pp 1097–1105
Guo Y, Liu Y, Oerlemans A, Lao S, We S, Lew MS (2016) Deep Learning for visual understanding: a review. Neurocomputing 187:27–48
Han J, Kamber M, Pei J (2012) Data mining. Concepts and techniques, Third ed. Morgan Kaufmann Publishers
Kumar AM, Gopal M A hybrid SVM based decision tree. Pat Rec 43:3977–3987
Alexandros I, Moncef G (2016) multi-class support vector machine classifiers using intrinsic and penalty graphs. Pat Rec 55:231–246
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jyothi, V.K., Guru, D.S., Kumar, N.V., Manjunath Aradhya, V.N. (2021). Retrieval of Videos of Flowers Using Deep Features. In: Suma, V., Bouhmala, N., Wang, H. (eds) Evolutionary Computing and Mobile Sustainable Networks. Lecture Notes on Data Engineering and Communications Technologies, vol 53. Springer, Singapore. https://doi.org/10.1007/978-981-15-5258-8_56
Download citation
DOI: https://doi.org/10.1007/978-981-15-5258-8_56
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5257-1
Online ISBN: 978-981-15-5258-8
eBook Packages: EngineeringEngineering (R0)