A Novel and Efficient CBIR using CNN for Flowers

Subash. S. I, Muthiah. M. A., N. Mathan
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Abstract

Image processing is vital to extract the required data from images. Machine learning is an efficient tool used for penetration in most of the classification and identification tasks performed by a computer. This project proposes the identification of a flower after the classification of flower images using a successful artificial intelligence tool named the Convolutional Neural Network (CNN). Models similar to this project have been used in most search engines for a long time, but CBIR (content-based image retrieval) still runs with less accuracy and produces outputs with fewer specifications due to the use of convolutional feed-forward networks for image retrieval. System performance depends a lot on the drawn-out features extracted from images. So, it is required to develop a CBIR system that retrieves similar images without explicit feature extraction and classification by using CNN, which accepts images as input. For experimentation, images from the Oxford-102 flower dataset are used.
一种新颖高效的基于CNN的花卉CBIR
图像处理对于从图像中提取所需的数据至关重要。机器学习是一种有效的工具,用于渗透大多数由计算机执行的分类和识别任务。这个项目提出了一个成功的人工智能工具——卷积神经网络(CNN),在对花的图像进行分类后,对花进行识别。与此项目类似的模型已经在大多数搜索引擎中使用了很长时间,但是由于使用卷积前馈网络进行图像检索,CBIR(基于内容的图像检索)仍然以较低的精度运行,并且产生的输出规格较少。系统性能在很大程度上取决于从图像中提取的拖长特征。因此,需要开发一种CBIR系统,使用CNN接受图像作为输入,在不进行明确特征提取和分类的情况下检索相似的图像。在实验中,使用了来自Oxford-102花卉数据集的图像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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