Deep learned vectors’ formation using auto-correlation, scaling, and derivations with CNN for complex and huge image retrieval

Abstract

AbstractDeep learning for image retrieval has been used in this era, but image retrieval with the highest accuracy is the biggest challenge, which still lacks auto-correlation for feature extraction and description. In this paper, a novel deep learning technique for achieving highly accurate results for image retrieval is proposed, which implements a convolutional neural network with auto-correlation, gradient computation, scaling, filter, and localization coupled with state-of-the-art content-based image retrieval methods. For this purpose, novel image features are fused with signatures produced by the VGG-16. In the initial step, images from rectangular neighboring key points are auto-correlated. The image smoothing is achieved by computing intensities according to the local gradient. The result of Gaussian approximation with the lowest scale and suppression is adjusted by the by-box filter with the standard deviation adjusted to the lowest scale. The parameterized images are smoothed at different scales at various levels to achieve high accuracy. The principal component analysis has been used to reduce feature vectors and combine them with the VGG features. These features are integrated with the spatial color coordinates to represent color channels. This experimentation has been performed on Cifar-100, Cifar-10, Tropical fruits, 17 Flowers, Oxford, and Corel-1000 datasets. This study has achieved an extraordinary result for the Cifar-10 and Cifar-100 datasets. Similarly, the results of the study have shown efficient results for texture datasets of 17 Flowers and Tropical fruits. Moreover, when compared to state-of-the-art approaches, this research produced outstanding results for the Corel-1000 dataset.