DEEP LEARNING IN ANIMAL CLASSIFICATION SYSTEM
DOI:
https://doi.org/10.29121/shodhkosh.v5.i2.2024.2677Keywords:
Texture, Shape, Color, SVM, CNNAbstract [English]
In this work, we propose a method for the classification of animal in images. Initially, a region merging segmentation method is used to perform segmentation in order to eliminate the background from the given image. From segmented animal images, the shape, texture and color features are extracted. Deep learning with Convolution Network and Support vector is considered for classification. To corroborate the efficacy of the proposed method, an experiment was conducted on our own data set of 30 classes of animals, which consisted of 600 sample images. The experiment was conducted by picking images randomly from the database to study the effect of classification accuracy, and the results show that the Deep Learning classifier achieves good performance.
References
Sharath Kumar Y. H.1, Manohar N.2, Hemantha Kumar G.2,” Supervised and Unsupervised Learning in Animal Classification”. © Springer-Verlag Berlin Heidelberg 2011.
Manohar N1, Subrahmanya S2, Bharathi R K2, Sharath Kumar Y H3, Hemantha Kumar G1,” Recognition and Classification of Animals based on Texture Features through Parallel Computing”. 2016 Second International Conference on Cognitive Computing and Information Processing (CCIP). DOI: https://doi.org/10.1109/CCIP.2016.7802872
Y H Sharath Kumar, Manohar N, Chethan H K,” Animal Classification System: A Block Based Approach”, International Conference on Advanced Computing Technologies and Applications (ICACTA2015).
Mohammed Nazir Alli Serestina Viriri, ”Animal Identification Based on Footprint Recognition”. IEEE 2013.
OlivierL´evesqueandRobertBergevin,“DetectionandIdentificationofAnimalsUsingStereoVision”. IEEE 2014.
Zheng Cao, Jose C,”Marine Animal Classification Using Combined CNN and Hand-designed Image Features”. IEEE 2015. DOI: https://doi.org/10.23919/OCEANS.2015.7404375
Slavomir Matuska,” A Novel System for Automatic Detection and Classification of Animal”.IEEE 2014. DOI: https://doi.org/10.1109/ELEKTRO.2014.6847875
Emmanuel Okafor,” Operational Data Augmentation in Classifying Single Aerial Images of Animals”. 2017 European Union. DOI: https://doi.org/10.1109/INISTA.2017.8001185
Sachin Umesh Sharma “A Practical Animal Detection and Collision Avoidance System Using Computer Vision Technique” IEEE 2017. DOI: https://doi.org/10.1109/ACCESS.2016.2642981
Yi Zhong, Zheng Zhou”classification of animals and people based on radio sensor network”. IEEE 2013.
Wenbing Tao, Hai Jin,” Color Image Segmentation Based on Mean Shift and Normalized Cuts”. IEEE 2007.
Giampaolo Pagnutti, Pietro Zanuttigh.” Joint segmentation of color and depth data based on splitting and merging driven by surface fitting “. elsevier 2018. DOI: https://doi.org/10.1016/j.imavis.2017.12.004
Yanhui Guo,” An effective color image segmentation approach using neutrosophic adaptive mean shift clustering”. Elsevier 2018.
Koen E. A. van de Sande,” Segmentationas Selective Search for Object Recognition ”.internal journal 2007.
Juan Zhu,” SIFT Method for Paper Detection System”. IEEE 2011. DOI: https://doi.org/10.1109/ICMT.2011.6001649
Vandana Vinayak,” CBIR System using Color Moment and Color Auto-Correlogram with Block Truncation Coding”. International Journal of Computer Applications 2017. DOI: https://doi.org/10.5120/ijca2017913282
ForrestN.Iandola,” SQUEEZENET: ALEXNET-LEVEL ACCURACY WITH 50X FEWER PARAMETERS AND <0.5MB MODEL SIZE”. Under review as a conference paper at ICLR 2017.
Dimitrios Marmanis, Mihai Datcu,” Deep Learning Earth Observation Classification Using ImageNet Pretrained Networks”. IEEE 2016. DOI: https://doi.org/10.1109/LGRS.2015.2499239
Greg Mori, Serge Belongie,” Efficient Shape Matching Using ShapeContexts”. IEEE 2005. DOI: https://doi.org/10.1109/TPAMI.2005.220
Maria-Elena Nilsback,” Automated flower classification over a large number of classes” . ICCV 2007. DOI: https://doi.org/10.1109/ICVGIP.2008.47
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Copyright (c) 2024 Thenarasi V, Santhosh Kumar B N, Prakasha Raje Urs M, Dr. Sharath Kumar Y H, Rashmi R
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