Description

Flowers classification using Transfer Learning

Classification of images plays an important role in sorting the images into classes based on their similarities. Recently, the demands on classifying images according to their features have shown great interest in many areas such as digital library, searching engine, or any content-based image retrieval system with the advantage of advanced computer technologies. However, some of the data used for image classification includes unnecessary information such as noise and the influence of sun or light. Hence, this study was performed to reduce the unnecessary data obtained during the feature extraction phase prior to classify the images using neural network. In this study, flower image classification is based on the low-level features such as colour and texture to define and describe the image content. This study analyzes the classification’s performance of the dataset using Convolutinal neural network.

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