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Due to recent development in technology, there is an increase in the usage of digital cameras, smartphone, and Internet.After the successful implementation of the abovementioned models, CBIR and feature extraction approaches are applied in various applications such as medical image analysis, remote sensing, crime detection, video analysis, military surveillance, and textile industry.To provide a query image as an input is the main requirement of CBIR and it matches the visual contents of query image with the images that are placed in the archive, and closeness in the visual similarity in terms of image feature vector provides a base to find images with similar contents.Content-based image retrieval (CBIR) is a framework that can overcome the abovementioned problems as it is based on the visual analysis of contents that are part of the query image.In CBIR, low-level visual features (e.g., color, shape, texture, and spatial layout) are computed from the query and matching of these features is performed to sort the output .According to the literature, Query-By-Image Content (QBIC) and SIMPLicity are the examples of image retrieval models that are based on the extraction of low-level visual semantic.The user submits a query by entering some text or keywords that are matched with the keywords that are placed in the archive.


Original text

Due to recent development in technology, there is an increase in the usage of digital cameras, smartphone, and Internet. The shared and stored multimedia data are growing, and to search or to retrieve a relevant image from an archive is a challenging research problem . The fundamental need of any image retrieval model is to search and arrange the images that are in a visual semantic relationship with the query given by the user. Most of the search engines on the Internet retrieve the images on the basis of text-based approaches that require captions as input . The user submits a query by entering some text or keywords that are matched with the keywords that are placed in the archive. The output is generated on the basis of matching in keywords, and this process can retrieve the images that are not relevant. The difference in human visual perception and manual labeling is the main reason for generating the output that is irrelevant . It is near to impossible to apply the concept of manual labeling to existing large size image archives that contain millions of images. The second approach for image retrieval and analysis is to apply an automatic image annotation system that can label image on the basis of image contents. The approaches based on automatic image annotation are dependent on how accurate a system is in detecting color, edges, texture, spatial layout, and shape-related information . Significant research is being performed in this area to enhance the performance of automatic image annotation, but the difference in visual perception can mislead the retrieval process. Content-based image retrieval (CBIR) is a framework that can overcome the abovementioned problems as it is based on the visual analysis of contents that are part of the query image. To provide a query image as an input is the main requirement of CBIR and it matches the visual contents of query image with the images that are placed in the archive, and closeness in the visual similarity in terms of image feature vector provides a base to find images with similar contents. In CBIR, low-level visual features (e.g., color, shape, texture, and spatial layout) are computed from the query and matching of these features is performed to sort the output . According to the literature, Query-By-Image Content (QBIC) and SIMPLicity are the examples of image retrieval models that are based on the extraction of low-level visual semantic. After the successful implementation of the abovementioned models, CBIR and feature extraction approaches are applied in various applications such as medical image analysis, remote sensing, crime detection, video analysis, military surveillance, and textile industry.

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