An Efficient Flower Classification System using Feature Fusion

An Efficient Flower Classification System using Feature Fusion
  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-11
Year of Publication : 2022
Authors : M. R. Banwaskar, A. M. Rajurkar
DOI : 10.14445/22315381/IJETT-V70I11P207

How to Cite?

M. R. Banwaskar, A. M. Rajurkar, "An Efficient Flower Classification System using Feature Fusion," International Journal of Engineering Trends and Technology, vol. 70, no. 11, pp. 70-80, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I11P207

Abstract
Automatic classification of flowers is essential in research on flowers, medicinal use of flowers, flower patent analysis etc. Traditionally, flower classification is done using low-level features like color, shape, texture and geometry. There exist large intra-class variation and interclass similarity among flower classes. Search engine-based flower identification and classification system are not efficient and robust because they are based on visual search. The accuracy and robustness of flower classification depend highly on the feature descriptor. Deep features have shown excellent performance in the last few years on high-resolution images, but they cannot extract accurate global features from low-resolution images. Hence, an efficient flower classification system using a fusion of handcrafted features and in-depth features is proposed in this paper. Low-level features are extracted using Color Coherent Vector (CCV), Centre Symmetric Local Binary Pattern (CSLBP) and Edge Histogram Descriptor (EHD). Deep features are extracted from pre-trained networks: ResNet-50 and AlexNet. Further, a Multiclass Support Vector Machine (SVM) is used to yield high classification accuracy. Experiments are carried out on Oxford Flower 17, Flower102, Kaggle flower dataset and Corel-1K dataset. Classification accuracy of 100, 95.3, 94 and 92% is obtained on the Corel dataset, Oxford Flower 17, Kaggle flower dataset and flower 102 dataset, respectively, which is better than existing approaches. A remarkable achievement in classification accuracy of 86.4% is observed on the pooled dataset.

Keywords
Deep Learning, Image descriptor, Convolutional Neural Networks, Image classification, Pooled dataset.

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