An efficient image retrieval based on an integration of HSV, RLBP, and CENTRIST features using ensemble classifier learn
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An efficient image retrieval based on an integration of HSV, RLBP, and CENTRIST features using ensemble classifier learning Navid Danapur, et al. [full author details at the end of the article] Received: 3 June 2019 / Revised: 8 May 2020 / Accepted: 27 May 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Recently, we have been witnessing a tremendous rise in digital image quantities, which in return calls for an adjustment and management system to fulfill user’s queries in the shortest time with maximum accuracy. In this regard, Content-Based Image Retrieval (CBIR) approaches have gained unprecedented attention. In CBIR systems, image search is based on their actual contents instead of textual annotations. Due to the fact that users do not think of low-level image features such as color, texture, structure, and shape and are looking for high-level image features or semantic features while querying images, the performance of image retrieval systems becomes weak. On the one hand, the huge amount of extracted features and the complexity of feature spaces are considered as two main challenges in image retrieval study area. Therefore, this article trying to extract the key features of the image in order to increase the accuracy and speed of image recovery over big data. This study combines two feature extraction techniques namely Census Transform Histogram (CENTRIST) and Rotated Local Binary Pattern (RLBP) following by Kernel Principal Component Analysis (PCA) method to reduce the dimensional feature space. In fact, after feature extraction phase we utilize Adaboost M2 classifying method on the train data to learn the different classes of images that are existed in the database. Furthermore, instead of using RGB color space, images are transformed to HSV color space. The reason for using the HSV color space instead of the RGB one is the fact that it is closer to human perception. Performance evaluations of the proposed method are conducted on the Corel-1 K and UW datasets. Simulation results indicate that proposed method performs better than other methods. Keywords Content based image retrieval . Feature extraction . HSV feature . Texture feature . Structural feature . RLBP . CENTRIST . And PCA
1 Introduction With respect to the exponential growth of digital images, image management and organization requires a tool to retrieve the user query image within the shortest time and maximum precision
Multimedia Tools and Applications
possible. In recent years, image retrieval methods have therefore become especially important [42, 65, 91, and]. The purpose of computer system image retrieval is to review, search, and recover images from a large–scale digital images database [23]. Main mage retrieval approaches can be classified into two categories: Text-Based Image Retrieval (TBIR) and Content-Based Image Retrieval (CBIR) [72]. In TBIR approach, a keyword, a subtitle, or a description as an ultradata is added to each of the images available in the database, which is added to images manual
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