Abstract
Content based image retrieval (CBIR) systems is a common recent method for image retrieval and is based mainly on two pillars extracted features and similarity measures. Low level image presentations, based on colour, texture and shape properties are the most common feature extraction methods used by traditional CBIR systems. Since these traditional handcrafted features require good prior domain knowledge, inaccurate features used for this type of CBIR systems may widen the semantic gap and could lead to very poor performance retrieval results. Hence, features extraction methods, which are independent of domain knowledge and have automatic learning capabilities from input image are highly useful. Recently, pre-trained deep convolution neural networks (CNN) with transfer learning facilities have ability to generate and extract accurate and expressive features from image data. Unlike other types of deep CNN models which require huge amount of data and massive processing time for training purposes, the pre-trained CNN models have already trained for thousands of classes of large-scale data, including huge images and their information could be easily used and transferred. ResNet18 and SqueezeNet are successful and effective examples of pre-trained CNN models used recently in many machine learning applications, such as classification, clustering and object recognition. In this study, we have developed CBIR systems based on features extracted using ResNet18 and SqueezeNet pre-trained CNN models. Here, we have utilized these pre-trained CNN models to extract two groups of features that are stored separately and then later are used for online image searching and retrieval. Experimental results on two popular image datasets Core-1K and GHIM-10K show that ResNet18 features based on the CBIR method have overall accuracy of 95.5% and 93.9% for the two datasets, respectively, which greatly outperformed the traditional handcraft features based on the CBIR method.
Highlights
The great development of digital computers and various smart devices, in addition to the large and steady increase in the different storage media, led to a considerable increase in digital images and other types of multimedia components
The efficiency and effectiveness of any content-based image retrieval (CBIR) system depends on the extracted features because it will be used as numerical values in calculating similarity between the query submitted by the end user and all the images stored in a repositories or data storage [3]
We implemented and tested the CBIR method based on features extracted from two different pretrained deep convolutional neural network (CNN) models
Summary
The great development of digital computers and various smart devices, in addition to the large and steady increase in the different storage media, led to a considerable increase in digital images and other types of multimedia components. The superior ability of pre-trained networks are the www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol 12, No 7, 2021 result of their training on large-scale images for a large number of classes This facility enables users to benefit from the advantage of pre-training and the transfer learning concept in various processes of classification or feature extraction. Due to the success and good performance of this type of neural network, in this study we propose the CBIR method that is based on the two popular types of these networks in extracting features, which will be used to retrieve images through their content.
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