Abstract
Content-Based Image Retrieval (CBIR) is a process of searching for an image according to the content or feature that is within it. Nowadays, most image retrieval applications have been developed to meet these needs, so this application will provide comfort in introducing and searching for an image. This paper proposed a standard structured framework with three stages: Preprocessing is the first step, in which noise from images is removed using various filters. The filters' results are compared to determine the best and most appropriate filter for the images. Feature Extraction of images using Curvelet Transform is the second stage. The third stage includes similarity measurement between query image features to database image features and extracting the identical image from the image dataset. The system was performed using Matlab 2017b, GUI and, with ten different classes of 1000 images using a coral database. The results show improved performance of precision and recall when higher decomposition levels are used.
Highlights
Images make websites more interesting, and humans can process and understand images faster than reading text
The first one is based on a search by metadata known as Text Base Image Retrieval (TBIR), and The second approach is based on Content information in the image, known as Content-Based Image Retrieval (CBIR)
CBIR is known as Query By Image Content (QBIC) [4], presents a flexible way to index images automatically based on the visual content of images
Summary
Images make websites more interesting, and humans can process and understand images faster than reading text. The first one is based on a search by metadata known as Text Base Image Retrieval (TBIR), and The second approach is based on Content information in the image, known as Content-Based Image Retrieval (CBIR). In the TBIR method, users use a keyword or description of images as a query to retrieve images that are related to the keyword [3]. Spectral approaches of texture analysis for image retrieval are robust to noise. The spectral approaches include Fourier transform, multiresolution methods such as Gabor filters, and wavelet transform for texture representation [9]. The disadvantage of these spectral methods is that they do not effectively capture image edge information.
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