Image retrieval approach based on local texture information derived from predefined patterns and spatial domain information

With the development of Information technology and communication, a large part of the databases is dedicated to images and videos. Thus retrieving images related to a query image from a large database has become an important area of research in computer vision. Until now, there are various methods of image retrieval that try to define image contents by texture, color or shape properties. In this paper, a method is presented for image retrieval based on a combination of local texture information derived from two different texture descriptors. First, the color channels of the input image are separated. The texture information is extracted using two descriptors such as evaluated local binary patterns and predefined pattern units. After extracting the features, the similarity matching is done based on distance criteria. The performance of the proposed method is evaluated in terms of precision and recall on the Simplicity database. The comparative results showed that the proposed approach offers higher precision rate than many known methods.

[1]  Malay Kumar Kundu,et al.  Content-based image retrieval using visually significant point features , 2009, Fuzzy Sets Syst..

[2]  Shervan Fekri Ershad,et al.  Color Texture Classification Approach Based on Combination of Primitive Pattern Units and Statistical Features , 2011, ArXiv.

[3]  Ashok K. Sinha,et al.  A Novel Approach for Content Based Image Retrieval , 2012 .

[4]  Wei Luo,et al.  Study on image retrieval based on image texture and color statistical projection , 2016, Neurocomputing.

[5]  Lu Liu,et al.  Content-based image retrieval using color and texture fused features , 2011, Math. Comput. Model..

[6]  Erchan Aptoula,et al.  Content based image retrieval of remote sensing images based on deep features , 2018, 2018 26th Signal Processing and Communications Applications Conference (SIU).

[7]  Mohammad Saberi,et al.  Developing a novel approach for content based image retrieval using modified local binary patterns and morphological transform , 2015, Int. Arab J. Inf. Technol..

[8]  Shervan Fekri Ershad,et al.  Multi-Resolution and Noise-Resistant Surface Defect Detection Approach Using New Version of Local Binary Patterns , 2017, Appl. Artif. Intell..

[9]  P. P. Belagali,et al.  Image Retrieval Technique Using Local Binary Pattern ( LBP ) , 2015 .

[10]  Chandan Singh,et al.  A fast and efficient image retrieval system based on color and texture features , 2016, J. Vis. Commun. Image Represent..

[11]  R. B. Bahaweres,et al.  Batik image retrieval based on similarity of shape and texture characteristics , 2012, 2012 International Conference on Advanced Computer Science and Information Systems (ICACSIS).

[12]  Ryszard S. Choras,et al.  Integrated color, texture and shape information for content-based image retrieval , 2007, Pattern Analysis and Applications.

[13]  Michael Felsberg,et al.  Compact color-texture description for texture classification , 2015, Pattern Recognit. Lett..

[14]  Sabine Süsstrunk,et al.  Deep Feature Factorization for Content-Based Image Retrieval and Localization , 2019, 2019 IEEE International Conference on Image Processing (ICIP).

[15]  Matti Pietikäinen,et al.  Rotation-invariant texture classification using feature distributions , 2000, Pattern Recognit..

[16]  Shervan Fekri Ershad,et al.  Content-based image retrieval based on combination of texture and colour information extracted in spatial and frequency domains , 2019, Electron. Libr..

[17]  Farshad Tajeripour,et al.  Developing a Novel Approach for Stone Porosity Computing Using Modified Local Binary Patterns and Single Scale Retinex , 2014 .

[18]  Anastasios Tefas,et al.  Deep convolutional learning for Content Based Image Retrieval , 2018, Neurocomputing.

[19]  Giorgio Giacinto,et al.  Information fusion in content based image retrieval: A comprehensive overview , 2017, Inf. Fusion.

[20]  Deren Li,et al.  A multi-scale and multi-orientation image retrieval method based on rotation-invariant texture features , 2011, Science China Information Sciences.

[21]  Khawaja Tehseen Ahmed,et al.  Content based image retrieval using image features information fusion , 2019, Inf. Fusion.

[22]  Xiangyang Wang,et al.  Content-based image retrieval by integrating color and texture features , 2012, Multimedia Tools and Applications.

[23]  Özgür Ulusoy,et al.  A histogram-based approach for object-based query-by-shape-and-color in image and video databases , 2005, Image Vis. Comput..

[24]  S Neetu Sharma,et al.  Efficient Cbir Using Color Histogram Processing , 2012 .

[25]  Shervan Fekri-Ershad,et al.  A Review on Image Texture Analysis Methods , 2018, 1804.00494.

[26]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Hassan Rashidi,et al.  A multi-objectives scheduling algorithm based on cuckoo optimization for task allocation problem at compile time in heterogeneous systems , 2016, Expert Syst. Appl..

[28]  Lei Zhang,et al.  Contents lists available at ScienceDirect Pattern Recognition , 2022 .

[29]  Paolo Napoletano,et al.  Visual descriptors for content-based retrieval of remote-sensing images , 2016, ArXiv.

[30]  Chuen-Horng Lin,et al.  Image Retrieval System Based on Adaptive Color Histogram and Texture Features , 2011, Comput. J..

[31]  Matti Pietikäinen,et al.  Texture Classification using a Linear Configuration Model based Descriptor , 2011, BMVC.