Content Based Image Retrieval using Nature Inspired Algorithms
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Bester Preis: € 47,99 (vom 28.01.2023)1
Content Based Image Retrieval using Nature Inspired Algorithms
PB NWISBN: 9786313909483 bzw. 6313909488, Sprache unbekannt, Vikatan Publishing Solutions, Taschenbuch, neu.
Lieferung aus: Deutschland, zzgl. Versandkosten.
Content-based image retrieval (CBIR) is a technique used to search for images in a database based on their visual content. CBIR systems are designed to retrieve images that are similar to a query image based on features such as color, texture, shape, and other visual characteristics. Nature-inspired algorithms are computational methods that are modeled on natural systems, such as genetic algorithms, particle swarm optimization, and ant colony optimization. These algorithms can be used in image retrieval systems to improve the accuracy and efficiency of the search process. For example, a genetic algorithm can be used to optimize the feature extraction process, which is an important step in CBIR systems. The algorithm can be used to evolve a set of features that are highly correlated with the visual characteristics of the images in the database, making it easier to find similar images. Particle swarm optimization (PSO) can be used to optimize the similarity measure, which is used to compare the query image to the images in the database. PSO can be used to find the optimal combination of features and similarity measure that results in the most accurate search results. Ant colony optimization (ACO) can be used to optimize the image search process. In this approach, the system simulates the behavior of ants, which are able to find the shortest path between two points. The algorithm can be used to find the most relevant images in the database by simulating the behavior of ants as they search for images that are similar to the query image. Overall, using nature-inspired algorithms in CBIR systems can improve the accuracy and efficiency of the image retrieval process. These algorithms can be used to optimize various steps in the process such as feature extraction, similarity measure, and search process, which can help to find the most relevant images in the database more efficiently. With the advancement of the low-cost digital recording and storage devices and social media, the amount of information, especially in the form of digital images and videos, increases with explosive rate per minutes. Low-priced mobile devices with build in camera feature, further increases the amount of multimedia information many folds. Therefore, a method to search this multimedia information was needed. The initial solution arose in the form of annotating the images, videos and audio with keywords and then use the existing technique of text retrieval. Many approaches were proposed with keywords-based image searches in [50], [63]. But to annotate the data manually was a very tedious task. Hence, research efforts were started to develop a mechanism that can categories the huge digital multimedia data automatically by analyzing the content of the data itself in such a way that when there is a need, the images and videos can be retrieved from the large database accurately and instantly. CBIR is the process of automatically indexing the images based on the visual features of the images, especially low-level visual features.
Content-based image retrieval (CBIR) is a technique used to search for images in a database based on their visual content. CBIR systems are designed to retrieve images that are similar to a query image based on features such as color, texture, shape, and other visual characteristics. Nature-inspired algorithms are computational methods that are modeled on natural systems, such as genetic algorithms, particle swarm optimization, and ant colony optimization. These algorithms can be used in image retrieval systems to improve the accuracy and efficiency of the search process. For example, a genetic algorithm can be used to optimize the feature extraction process, which is an important step in CBIR systems. The algorithm can be used to evolve a set of features that are highly correlated with the visual characteristics of the images in the database, making it easier to find similar images. Particle swarm optimization (PSO) can be used to optimize the similarity measure, which is used to compare the query image to the images in the database. PSO can be used to find the optimal combination of features and similarity measure that results in the most accurate search results. Ant colony optimization (ACO) can be used to optimize the image search process. In this approach, the system simulates the behavior of ants, which are able to find the shortest path between two points. The algorithm can be used to find the most relevant images in the database by simulating the behavior of ants as they search for images that are similar to the query image. Overall, using nature-inspired algorithms in CBIR systems can improve the accuracy and efficiency of the image retrieval process. These algorithms can be used to optimize various steps in the process such as feature extraction, similarity measure, and search process, which can help to find the most relevant images in the database more efficiently. With the advancement of the low-cost digital recording and storage devices and social media, the amount of information, especially in the form of digital images and videos, increases with explosive rate per minutes. Low-priced mobile devices with build in camera feature, further increases the amount of multimedia information many folds. Therefore, a method to search this multimedia information was needed. The initial solution arose in the form of annotating the images, videos and audio with keywords and then use the existing technique of text retrieval. Many approaches were proposed with keywords-based image searches in [50], [63]. But to annotate the data manually was a very tedious task. Hence, research efforts were started to develop a mechanism that can categories the huge digital multimedia data automatically by analyzing the content of the data itself in such a way that when there is a need, the images and videos can be retrieved from the large database accurately and instantly. CBIR is the process of automatically indexing the images based on the visual features of the images, especially low-level visual features.
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Content Based Image Retrieval using Nature Inspired Algorithms
DE PB NWISBN: 9786313909483 bzw. 6313909488, in Deutsch, Vikatan Publishing Solutions, Taschenbuch, neu.
Lieferung aus: Deutschland, zzgl. Versandkosten, Shipping in 3 days.
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