The International Arab Journal of Information Technology (IAJIT)

..............................
..............................
..............................


A Novel Machine-Learning Framework-based on

This paper presents a Multiple-features extraction and reduction-based approaches for Content-Based Image Retrieval (CBIR). Discrete Wavelet Transforms (DWT) on colored channels is used to decompose the image at multiple stages. The Gray Level Co-occurrence Matrix (GLCM) concept is used to extract statistical characteristics for texture image classification. The definition of shared knowledge is used to classify the most common features for all COREL dataset groups. These are also fed into a feature selector based on the particle swarm optimization which reduces the number of features that can be used during the classification stage. Three classifiers, called the Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Decision Tree (DT), are trained and tested, in which SVM give high classification accuracy and precise rates. In several of the COREL dataset types, experimental findings have demonstrated above 94 percent precision and 0.80 to 0.90 precision values.


[1] Ahonen T. Pietikinen M., “Soft Histograms for Local Binary Patterns,” Proceedings of the Finnish Signal Processing Symposium, vol. 5, no. 9, pp. 1-4, 2007.

[2] Amin A. and Qureshi M., “A Novel Image Retrieval Technique Using Automatic and Interactive Segmentation,” The International Arab Journal of Information Technology, vol. 17, no. 3, pp. 404-410, 2020.

[3] Bul S., Rabbi M., and Pelillo M., “Content-Based Image Retrieval with Relevance Feedback Using Random Walks,” Pattern Recognition, vol. 44, no. 9, pp. 2109-2122, 2011.

[4] Bay H., Ess A., Tuytelaars T., and Gool L., “Speeded-up Robust Features (SURF),” Computer Vision Image Understanding, vol. 110, no. 3, pp. 346-359, 2008.

[5] Bhukya R. and Ashok A., “Gene Expression Prediction Using Deep Neural Networks,” The International Arab Journal of Information Technology, vol. 17, no. 3, pp. 422-431, 2020.

[6] Chang H. and Yeung D., “Locally Linear Metric Adaptation with Application to Semi-Supervised Clustering and Image Retrieval,” Pattern Recognition, vol. 39, no. 7, pp.1253-1264, 2006.

[7] Chandrasekhar V., Takacs G., Chen D., Tsai S., Grzeszczuk R., and Girod B., “CHog: Compressed Histogram of Gradients A Low Bit- Rate Feature Descriptor,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,” Miami, pp. 2504-2511, 2009.

[8] Chandrasekhar V., Reznik Y., Takacs G., Chen D., Tsai S., Grzeszczuk R., and Girod B., “Quantization Schemes for Low Bitrate Compressed Histogram of Gradients De- Scriptors,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, San Francisco, pp. 33-40, 2010.

[9] Dalal N. and Triggs B., “Histograms of Oriented Gradients for Human Detection,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, pp. 886-893, 2005. 0 0.2 0.4 0.6 0.8 1 1.2 PrecisionRecallF-MeasureAccuracy 0 0.2 0.4 0.6 0.8 1 PrecisionRecallF-MeasureAccuracy 0 0.2 0.4 0.6 0.8 1 1.2 PrecisionRecallF-MeasureAccuracy 304 The International Arab Journal of Information Technology, Vol. 18, No. 3, May 2021

[10] Dalal N., and Tariggs B., and Schmid C., “Human Detection Using Oriented Histograms of Flow and Appearance,” in Proceedings of European Conference on Computer Vision, Graz, pp. 428-441, 2006.

[11] Dhiman G. and Vijay K., “Spotted Hyena Optimizer: A Novel Bio-Inspired Based Metaheuristic Technique for Engineering Applications,” Advances in Engineering Software, vol. 114, pp. 48-70, 2017.

[12] Dhiman G., Garg M., Nagar A., Kumar V., and Dehghani M., “A Novel Algorithm for Global Optimization: Rat Swarm Optimizer,” Journal of Ambient Intelligence and Humanized Computing, pp. 1-26, 2020.

[13] Dhiman G., Singh K., Slowik A., Chang V., Yildiz A., Kaur A., and Garg M., “EMoSOA: A New Evolutionary Multi-Objective Seagull Optimization Algorithm for Global Optimization,” International Journal of Machine Learning and Cybernetics, vol. 12, no. 7, pp. 571-596, 2020.

[14] Dhiman G. and Kumar V., “Emperor Penguin Optimizer: A Bio-Inspired Algorithm for Engineering Problems,” Knowledge-Based Systems, vol. 159, pp. 20-50, 2018.

[15] Dhiman G. and Kumar V., “Multi-Objective Spotted Hyena Optimizer: A Multi-Objective Optimization Algorithm for Engineering Problems,” Knowledge-Based Systems, vol. 150, pp. 175-197, 2018.

[16] Dhiman G. and Kumar V., “Seagull Optimization Algorithm: Theory and its Applications for Large-Scale Industrial Engineering Problems,” Knowledge-Based Systems, vol. 165, pp. 169-196, 2019.

[17] Dhiman G. and Kaur A., “Stoa: A Bio-Inspired Based Optimization Algorithm for Industrial Engineering Problems,” Engineering Applications of Artificial Intelligence, vol. 82, pp. 148-174, 2019.

[18] Dhiman G., “ESA: a Hybrid Bio-Inspired Metaheuristic Optimization Approach for Engineering Problems,” Engineering with Computers, pp. 1-31, 2019.

[19] Dhiman G., “MOSHEPO: A Hybrid Multi- Objective Approach to Solve Economic Load Dispatch and Micro Grid Problems,” Applied Intelligence, vol. 50, no. 1, pp. 119-137, 2020.

[20] Dhiman G., “Multi-Objective Metaheuristic Approaches for Data Clustering in Engineering Application,” PhD Theses, Deemed University, 2019.

[21] Dhiman G., Soni M., Pandey H., Slowik A., and Kaur H., “A Novel Hybrid Hyper volume Indicator and Reference Vector Adaptation Strategies Based Evolutionary Algorithm for Many-Objective Optimization,” Engineering with Computers, pp. 1-19, 2020.

[22] Fan K. and Hung T., “A Novel Local Pattern Descriptor-Local Vector Pattern in High-Order Derivative Space for Face Recognition,” IEEE IEEE Transactions on Image Processing, vol. 23, no. 7, pp. 2877-2891, 2014.

[23] Guo Z., Zhang L., and Zhang D., “Rotation Invariant Texture Classification Using LBP Variance (LBPV) with Global Matching,” Pattern Recognition, vol. 43, no. 3, pp. 706-719, 2010.

[24] Guo Z., Zhang L., and Zhang D., “A Completed Modeling of Local Binary Pattern Operator for Texture Classification,” IEEE Transaction Image Processing, vol. 19, no. 6, pp. 1657-1663, 2010.

[25] Guo Z., Li Q., You J., Zhang D., and Liu W., “Local Directional Derivative Pattern for Rotation Invariant Texture Classification,” Neural Computer Application, vol. 21, no. 8, pp. 1893-1904, 2012.

[26] Garg M. and Dhiman G., “Deep Convolution Neural Network Approach for Defect Inspection of Textured Surfaces,” Journal of the Institute of Electronics and Computer, vol. 2, no. 1, pp. 28- 38, 2020.

[27] Garg M. and Dhiman G., “A Novel Content Based Image Retrieval Approach for Classification Using Glcm Features and Texture Fused Lbp Variants,” Neural Computing and Applications, pp. 1311-1328, 2020.

[28] Garg M., Malhotra M., and Singh H., “Comparison of Deep Learning Techniques on Content Based Image Retrieval,” Modern Physics Letters A, pp. 1950285, 2019.

[29] Garg M., Singh H., and Malhotra M., “Fuzzy-NN Approach with Statistical Features for Description and Classification of Efficient Image Retrieval,” Modern Physics Letters A, vol. 34, no. 03, pp. 1950022, 2019.

[30] Garg M., Malhotra M., and Singh H., “Statistical Feature Based Image Classification and Retrieval Using Trained Neural Classifiers,” International Journal of Applied Engineering Research, vol. 13, no. 8, pp. 5766-5771, 2018.

[31] Iakovidis D., Keramidas E., and Maroulis D., “Fuzzy Local Binary Patterns for Ultrasound Texture Characterization,” in Proceedings of International Conference Image Analysis and Recognition, Póvoa de Varzim, pp. 750-759, 2008.

[32] Kundu M., Chowdhury M., and Bulș S., “A graph-Based Relevance Feedback Mechanism in Content-Based Image Retrieval,” Knowledge Based System, vol. 73, pp. 254-264, 2015.

[33] Kaur S., Awasthi L., Sangal A., and Dhiman G., “Tunicate Swarm Algorithm: A New Bio- Inspired Based Metaheuristic Paradigm for Global Optimization,” Engineering Applications A Novel Machine-Learning Framework-based on LBP and GLCM Approaches for CBIR System 305 of Artificial Intelligence, vol. 90, pp. 103541, 2020.

[34] Lowe D., “Distinctive Image Features from Scale-Invariant Key Points,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.

[35] Liao S., Law M., and Chung A., “Dominant Local Binary Patterns for Texture Classification,” IEEE Transactions Image Processing, vol. 18, no. 5, pp. 1107-1118, 2009.

[36] Liu L., Zhao L., Long Y., Kuang G., and Fieguth P., “Extended Local Binary Patterns for Texture Classification,” Image and Vision Computing, vol. 30, no. 2, pp. 86-99, 2012.

[37] Liu L., Lao S., Fieguth P., Guo Y., Wang X., and Pietikäinen M., “Median Robust Extended Local Binary Pattern for Texture Classification,” IEEE Transactions on Image Processing, vol. 25, no. 3, pp. 1368-1381, 2016.

[38] Mehta R. and Egiazarian K., “Dominant Rotated Local Binary Patterns (DRLBP) for Texture Classification,” Pattern Recognition Letters, vol. 71, no. 3, pp. 16-22, 2016.

[39] Ojala T., Pietikainen M., and Harwood D., “Performance Evaluation of Texture Measures with Classification Based on Kullback Discrimination of Distributions,” in Proceedings of 12th International Conference on Pattern Recognition, Jerusalem, pp. 582-585, 1994.

[40] Oberoi A., Bakshi V., Sharma R., and Singh M., “A Framework for Medical Image Retrieval Using Local Tetra Patterns,” International Journal Engineering Technology, vol. 5, no. 1, pp. 27-36, 2013.

[41] Schettini R., Ciocca G., and Gagliardi I., “Feature Extraction for Content-Based Image Retrieval,” Encyclopedia of Database Systems, pp. 1115-1119, 2009.

[42] Su S., Chen S., Li S., Li S., and Duh D., “Structured Local Binary Haar Pattern for Pixel- Based Graphics Retrieval,” IET Electronic Letter, vol. 46, no. 14, pp. 996-998, 2010.

[43] Srivastava P. and Khare A., “Integration of Wavelet Transform Using Local Binary Patterns And Moments for Content-Based Image Retrieval,” Journal of Visual Communication and Image Representation, vol. 42, pp. 78-103, 2017.

[44] Tan X. and Triggs B., “Enhanced Local Texture Feature Sets for Face Recognition under Difficult Lighting Conditions,” IEEE Transactions Image Process, vol. 19, no. 6, pp. 1635-1650, 2010.

[45] Verma M. and Raman B., “Center Symmetric Local Binary Co-Occurrence Pattern for Texture, Face and Bio-Medical Image Retrieval,” Journal of Visual Communication and Image Representation, vol. 32, pp. 224-236, 2015.

[46] Wang X., Han T., and Yan S., “A HOG-LBP Human Detector with Partial Occlusion Handling,” in Proceedings of IEEE 12th International Conference on Computer Vision, Kyoto, pp. 32-39, 2009.

[47] Yue J., Li Z., Lu L., and Fu Z., “Content-Based Image Retrieval Using Color and Texture Fused Features,” Mathematical and Computer Modelling, vol. 54, no. 3-4, pp. 1121-1127, 2011.

[48] Zhang B., Gao Y., Zhao S., and Liu J., “Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor,” IEEE Transactions on Image Processing, vol. 19, no. 2, pp. 533-544, 2010. Meenakshi Garg received her M.Tech. from MDU, Rohtak. She is expert in image processing and optimization fields. She has published research papers in various reputed journals. Currently, she is working as an Assistant Professor in Govt. Bikram College of Commerce, Patiala. Manisha Malhotra received her Ph.D. from Maharishi Markandeshwar University, Mullana. She is expert in image processing, deep learning, and optimization fields. She has published more than 20 research papers in various indexed journals. Currently, she is working as an Professor in Chandigarh University, Gharaun, Chandigarh. Harpal Singh received his Ph.D. from Punjabi University, Patiala. He is expert in image processing, deep learning, and optimization fields. He has published more than 10 research papers in various indexed journals. Currently, he is working as Professor in Chandigarh Engineering College, Landran, Chandigarh.