Pre-trained CNNs Models for Content based Image Retrieval

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. ResNetl8 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 ResNetl8 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 ResNetl8 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.