Abstract
In this paper, a model for the classification of the flowers in videos is proposed. Flowers in videos are segmented using the Otsu threshold segmentation technique. Histogram of oriented texture features is extracted from segmented flowers. Further, Principal Component Analysis (PCA) has been used to select the discriminating features and for dimensionality reduction. Nearest Neighbor (NN) classifier is used for the classification. The efficiency of the proposed system is ascertained using the dataset which consists of ten different classes of flower videos. The dataset exhibits large intra-class variation with less inter-class similarity. Comparative analysis demonstrates the efficacy of the proposed model.
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
References
Wäldchen J, Rzanny M, Seeland M, Mäder P (2018) Automated plant species identification-trends and future directions. PLoS Comput Biol e1005993
Das M, Manmatha R, Riesman E (1999) Indexing flower patent images using domain knowledge. IEEE Intell Syst 24–33
Patil AB, Shaikh JA (2016) OTSU thresholding method for flower image segmentation. Int J Comput Eng Res 2250–3005
Cıbuk M, Budak U, Guo Y, Ince MC, Sengur A (2019) Efficient deep features selections and classification for flower species recognition. Measurement 7–13
Guru DS, Sharath YH, Manjunath S (2010) Texture features and KNN in classification of flower images. In: IJCA special issue on recent trends in image processing and pattern recognition, pp 21–29
Cheng K, Tan X (2014) Sparse representations based attribute learning for flower classification. Neurocomputing 416–426
Kulkarni T, Uke NJ (2014) Implementation of image based flower classification system. Int J Comput Sci Bus Inform 35–44
Tiay T, Benyaphaichit P, Riyamongkol P (2014) Flower recognition system based on image processing. In: Third ICT international student project conference, pp 99–102
Qi W, Liu X, Zhao J (2012) Flower classification based on local and spatial visual cues. In: IEEE international conference on computer science and automation engineering, pp 670–674
Shi X (2017) Research on flower image classification using sum-product networks. In: 13th international conference on computational intelligence and security, pp 107–110
Rani R, Kumar R, Singh AP (2018) An empirical evaluation of translational and rotational invariance of descriptors and the classification of flower dataset. Pattern Anal Appl 1–18
Lodh A, Parekh R (2017) Flower recognition system based on color and GIST feature. In: Device for integrated circuit, pp 790–794
Soleimanipour A, Chegini GR, Massah J (2018) Classification of Anthurium flowers using combination of PCA, LDA and support vector machine. Agric Eng Int CIGR J 219–228
Togacar M, Ergen B, Comert Z (2020) Classification of flower species by using features extracted from the intersection of feature selection methods in convolutional neural network models. Measurement 107703
Sharath Kumar YH, Guru DS (2013) Classification of flowers: a symbolic approach. In: Proc. of Int. Conf. on Multimedia Processing, Communication and Info. Tech., pp 229–235
Jyothi VK, Guru DS, Sharath Kumar YH (2019) Classification of natural flower videos through sequential keyframe selection using SIFT and DCNN. In: recent trends in image processing and pattern recognition—second international conference, pp 305–318
Bangare SL, Dubal A, Bangare PS, Patil ST (2015) Reviewing Otsu’s method for image thresholding. Int J Appl Eng Res 21777–21783
Kobayashi T, Hidaka A, Kurita T (2008) Selection of histograms of oriented gradients features for pedestrian detection. Neural Inf Process 598–607
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chaitra, K.N., Jyothi, V.K., Chandrajit, M., Guru, D.S. (2022). Flower Classification in Videos: A HOG-PCA-NN Method. In: Mathur, G., Bundele, M., Lalwani, M., Paprzycki, M. (eds) Proceedings of 2nd International Conference on Artificial Intelligence: Advances and Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-6332-1_22
Download citation
DOI: https://doi.org/10.1007/978-981-16-6332-1_22
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-6331-4
Online ISBN: 978-981-16-6332-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)