Abstract
Due to the development of internet and digital media techniques the size of digital image collection is increasing rapidly. Various techniques for storing, searching and retrieving images become essential for large image archives. This leads to an existence of Image Retrieval System. Content-Based Image Retrieval (CBIR) is a process of retrieving the expected images from large image databases based on the features of the query image and this is performed by various feature extraction techniques. In this work, two different methods of CBIR have been proposed. The first method is CBIR using Discrete Wavelet Transform (DWT), which extracts the texture feature of an image and histogram extracts the color feature of an image. And also the results will be ranked according to the chi-square distance calculation. The second method is CBIR using Convolutional Neural Network (CNN). In CNN method the features are extracted using VGG16 pre-trained model and canny edge detection is used for detecting the edges of an image. Finally, the performances of DWT and CNN based methods are analyzed and compared.
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