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Content Based Image Retrieval Project

Author: Alia Khan
by Alia Khan
Posted: May 21, 2021

Due to recent development in technology, there is an increase in the usage of digital cameras, smartphones, 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 re- lationship with the query given by the user Content Based Image Retrieval Project.

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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 di?erence in human visual perception and manual labeling/annotation 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 de- pendent on how accurate a system is in detecting color, edges, texture, spatial layout, and shape-related information [11–13]. Signi?cant research is being performed in this area to enhance the performance of automatic image annotation, but the di?erence in visual perception can mislead the re- trieval process. Content-based image retrieval (CBIR) is a framework that can overcome the abovementioned prob- lems 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?nd 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 [1]. 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 [1]. 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. Figure 1 provides an overview of the basic concepts and mechanism of image retrieval [14–16].

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 de- pendent on how accurate a system is in detecting color, edges, texture, spatial layout, and shape-related information [11–13]. Signi?cant research is being performed in this area to enhance the performance of automatic image annotation, but the di?erence in visual perception can mislead the re- trieval process. Content-based image retrieval (CBIR) is a framework that can overcome the abovementioned prob- lems 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?nd 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 [1]. 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 [1]. 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. Figure 1 provides an overview of the basic concepts and mechanism of image retrieval [14–16].
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Author: Alia Khan

Alia Khan

Member since: May 11, 2021
Published articles: 8

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