Cat Breeds Classification Using Convolutional Neural Network For Multi-Object Image

Naura Qatrunnada, Muhammad Fachrurrozi, Alvi Syahrini Utami

Abstract

Cat is one of the most popular pets. There are many cat breeds with unique characteristic and treatment for each breed. A cat owner can have more than one cat, either the same breed or different breeds.  But not all cat owners know the breeds of their cats. Computers can be trained to recognized cat breeds, but there are many challenges for computers because it limited by how much they have been trained and programmed. In recent years, a lot of research about image classification has been done before and got various result, but most of the data used in previous research were single object images. Therefore, this study of cat breeds classification would be conducted with Convolutional Neural Network (CNN) in the Multi-Object images. This method was chosen because it had good classification results in the previous studies. This study used 5 breeds of cats with every breed having 200-3200 images for training. The test results were measured using confusion matrix, obtaining the precision, recall, f1 score and accuracy of 100% on multi-object images with 2 objects and 3 objects. On images with 4 objects achieved the precision, recall, f1 score and accuracy value of 89%, 87%, 87% and 95%. While the value of precision, recall, f1 score and accuracy on images with 5 objects get 87%, 86%, 86% and 94%, respectively.

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References

Afif, M., Fawwaz, A., Ramadhani, K. N., & Sthevanie, F. (2021). Klasifikasi Ras pada Kucing menggunakan Algoritma Convolutional Neural Network ( CNN ). 8(1), 715–730.

Alam, I. F., Sarita, M. I., & Sajiah, A. M. (2019). METODE CONVOLUTIONAL NEURAL NETWORK. 5(2), 237–244.

Borwarnginn, P., Thongkanchorn, K., Kanchanapreechakorn, S., & Kusakunniran, W. (2019). Breakthrough Conventional Based Approach for Dog Breed Classification Using CNN with Transfer Learning. 2019 11th International Conference on Information Technology and Electrical Engineering, ICITEE 2019, 7, 1–5. https://doi.org/10.1109/ICITEED.2019.8929955

Chollet, F. (2014). Xception: Deep Learning with Depthwise Separable Convolutions.

Compton, W. C., & Hoffman, E. L. (2019). Positive Psychology: The Science of Happiness and Flourishing. SAGE Publications, Inc; 3rd edition.

Dimitriou, M., Kounalakis, T., Vidakis, N., & Triantafyllidis, G. (2013). Detection and Classification of Multiple Objects using an RGB-D Sensor and Linear Spatial Pyramid Matching. 12(2), 78–87.

Effendi, M. R. (2016). Sistem Deteksi Wajah Jenis Kucing dengan Image Classification menggunakan OpenCV.

Fachrurrozi, M., Fiqih, A., Saputra, B. R., & Algani, R. (2017). Content Based Image Retrieval for Multi-Objects Fruits Recognition using k-Means and k-Nearest Neighbor. 1–6.

Géron, A. (2017). Hands-On Machine Learning with Scikit-Learn (N. Tache (ed.); 1st ed.). O’Reilly Media, Inc.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. http://www.deeplearningbook.org

Hao, P., Ding, Y., & Fang, Y. (2008). Image Retrieval Based On Fuzzy Kernel Clustering and Invariant Moments. December 2008. https://doi.org/10.1109/IITA.2008.189

İmamoğlu, N., Eresen, A., & Özbayoglu, A. M. (2011). Hierarchical Rule-Based Neural Network for Multi-Object Classification Using Invariant Features. 2011 International Symposium on Innovations in Intelligent Systems and Applications, 296–299.

Indriyani, S., Sthevanie, F., & Ramadhani, K. N. (2019). Pengenalan Ras Kucing Scottish Fold Menggunakan Metode Histogram of Oriented Gradients dan Jaringan Saraf Tiruan. 6(2), 9325–9335.

Khan, R., & Debnath, R. (2019). Multi Class Fruit Classification Using Efficient Object Detection and Recognition Techniques. August. https://doi.org/10.5815/ijigsp.2019.08.01

Khan, S., Rahmani, H., Shah, S. A. A., & Bennamoun, M. (2018). A Guide to Convolutional Neural Networks for Computer Vision. Synthesis Lectures on Computer Vision, 8(1). https://doi.org/10.2200/s00822ed1v01y201712cov015

Nimmagadda, T., & Anandkumar, A. (2015). Multi-Object Classification and Unsupervised Scene Understanding Using Deep Learning Features and Latent Tree Probabilistic Models.

Putra, W. S. E. (2016). Klasifikasi Citra Menggunakan Convolutional Neural Network (CNN) pada Caltech 101. Jurnal Teknik ITS, 5(1). https://doi.org/10.12962/j23373539.v5i1.15696

Scherer, D., Andreas, M., & Behnke, S. (2010). Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition. September. https://doi.org/https://doi.org/10.1007/978-3-642-15825-4_10

Shi, H., & Wang, S. (2015). BVCNN : a multi-object image recognition method based on the convolutional neural networks. https://doi.org/10.1109/ICVRV.2015.28

Zhang, X., Yang, L., & Sinnott, R. (2019). A Mobile Application for Cat Detection and Breed Recognition Based on Deep Learning. AI4Mobile 2019 - 2019 IEEE 1st International Workshop on Artificial Intelligence for Mobile, 7–12. https://doi.org/10.1109/AI4Mobile.2019.8672684

DOI: https://doi.org/10.36706/sjia.v3i2.46

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