Flower Categorization using Deep Convolutional Neural Networks
This incremental work addresses flower categorization for botanists and camping enthusiasts, but it applies existing methods to a specific dataset without major innovations.
The paper tackled flower classification by comparing GoogLeNet and AlexNet on the Oxford 102-category flower dataset, achieving top-1 accuracies of 47.15% and 43.39%, respectively, which are significantly better than random chance.
We have developed a deep learning network for classification of different flowers. For this, we have used Visual Geometry Group's 102 category flower dataset having 8189 images of 102 different flowers from University of Oxford. The method is basically divided into two parts; Image segmentation and classification. We have compared the performance of two different Convolutional Neural Network architectures GoogLeNet and AlexNet for classification purpose. By keeping the hyper parameters same for both architectures, we have found that the top 1 and top 5 accuracies of GoogLeNet are 47.15% and 69.17% respectively whereas the top 1 and top 5 accuracies of AlexNet are 43.39% and 68.68% respectively. These results are extremely good when compared to random classification accuracy of 0.98%. This method for classification of flowers can be implemented in real time applications and can be used to help botanists for their research as well as camping enthusiasts.