Survey and Analysis of Content-Based Image Retrieval Systems
Content-based image retrieval (CBIR) systems find a lively application in various fields like medical diagnosis, crime prevention, art collection, textile industry, etc. CBIRs continue to be an active domain of research due to increasing image databases a
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Survey and Analysis of Content-Based Image Retrieval Systems Biswajit Jena, Gopal Krishna Nayak, and Sanjay Saxena
Introduction The advent of the Internet facilitated the exchange and querying of information. Over the years, the methodology adopted by users to query data has witnessed considerable changes primarily because of the querying and data retrieval mechanisms on userend getting easier, interactive, and friendly. The earlier years of the Internet era saw a greater amount of text-based data being generated, queried, and transferred but as the feature of multimedia got incorporated with the textual Web pages and applications; the shift has been toward image-based retrieval schemes. Earlier, this was brought forward by text-based image retrieval systems [1], where the visuals were annotated manually by textual phrases or words. When such a system was used to query a particular image from a large database of textually annotated images, it often would suffer from imprecision in the search results. This was chiefly because different humans might perceive an image differently. Also, the process of annotating an image was time-consuming and required a lot of human effort. To combat this issue, the CBIR was put forward in the early 1980s [2]. Since then, it has become a lively research area backed by a variety of different individual fields like pattern recognition, machine learning, computer vision, and databases to name a few. The fundamental goal of any CBIR system is feature extraction [1, 2]. An object in such a system is described in terms of its low-level features like texture, color, and shape. Often, human beings tend to identify an object from its color; thus, we can closely link color with the visual perception of an object in the human mind. To study this feature, a lot of techniques are applied to color perception and color spaces. Color B. Jena (B) · G. K. Nayak · S. Saxena International Institute of Information Technology, Bhubaneswar 751003, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. K. Singh and M. Tripathy (eds.), Control Applications in Modern Power System, Lecture Notes in Electrical Engineering 710, https://doi.org/10.1007/978-981-15-8815-0_37
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histograms are one of them. In this, the focus is on the distribution of color in an image regardless of the spatial location where that particular color might be found in an image. They are typically employed on three-dimensional color spaces like HSV and RGB and are extremely useful because of their flexibility, low computational complexity, and compact representation. Another important low-level feature is the texture which is responsible for defining the spatial positioning of colors or various intensities in an image. Gray-level co-occurrence matrix (GLCM), Gabor filter, wavelet transform, and curvelet transform are some ways of texture representation [6–9]. The shape-based features are extremely helpful yet a
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