Experimental analogy of different texture feature extraction techniques in image retrieval systems
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Experimental analogy of different texture feature extraction techniques in image retrieval systems Shefali Dhingra 1 & Poonam Bansal 2 Received: 22 June 2019 / Revised: 5 June 2020 / Accepted: 9 July 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Content based image retrieval (CBIR) is an extrusive technique of retrieving the relevant images from vast image archives by extracting their low level features. In this research paper, the pursuance of five most prominent texture feature extraction techniques used in CBIR systems are experimentally compared in detail. The main issue with the CBIR systems is the proper selection of techniques for the extraction of low level features which comprises of color, texture and shape. Among these features, texture is one of the most decisive and dominant features. This selection of features completely depends upon the type of images to be retrieved from the database. The texture techniques explored here are Grey level co-occurrence matrix (GLCM), Discrete wavelet transform (DWT), Gabor transform, Curvelet and Local binary pattern (LBP). These are experimented on three touchstone databases which are Wang, Corel-5 K and Corel-10 K. The chief parameters of CBIR systems are evaluated here such as precision, recall and F-measure on all these databases using all the techniques. After detailed investigation it is figured out that LBP, GLCM and DWT provide highlighted and comparable results in all these datasets in terms of average precision. Besides practical implementation, the précised conceptual examination of these three texture techniques is also proposed in this article. So, this analysis is extremely beneficial for selecting the appropriate feature extraction technique by taking into consideration the experimental results along with image conditions such as noise, rotation etc. Keywords Discrete wavelet transform . Local binary pattern . Precision . F-measure . Relevance feedback . Recall
* Shefali Dhingra [email protected] Poonam Bansal [email protected] Extended author information available on the last page of the article
Multimedia Tools and Applications
1 Introduction With the incredible up-gradation in the fields of image capturing devices, smart mobiles etc. there has been a considerable innovation in the area of image processing and image storage devices. Due to all this, there is the formation of different types of colossal image repositories. But dealing with these databases is a very challenging and tedious task. Consequently, for searching and indexing the images from these huge databases a competent and fast retrieval system is needed. Traditionally, retrieval of the images is done by text based image retrieval (TBIR) system, where textual description such as keywords or some text which describes the image is entered manually in the system [1]. From that particular word the similar images are finally retrieved. But this system suffers from several pit falls such as synonyms, homonyms, human annotation errors etc. Due
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