Geometrically transformed image retrieval with transfer learning
Keywords:
content-based image retrieval, CNN, ResNet18, VGG19, denseNet201Abstract
Image retrieval as per their features is an important topic since last decades. For image processing, classification and retrieval deep learning (DL) techniques utilized as state-of-the art techniques. For the images geometric transformation especially, rotation is common thing. However, Convolutional Neural Networks (CNNs) not supporting rotation invariance features due to use of anisotropic filters for convolution. So, it an important emerging field in image processing that, rotations in images should not affect on its characteristics or features. In this paper, we propose to use features pre-trained CNN model combinations, which trained for large image database containing rotated as well as scaled images for classification & similar image retrieval. This approach produces superior result of classification and retrieval for Corel dataset for original query images and comparably good results by considering rotated & scaled query images.
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