Content-Dependent Image Search System with Automatic Weighting Mechanism for Aggregating Color, Shape, and Texture Features

Authors

  • Arvita Agus Kurniasari Politeknik Negeri Jember
  • Ali Ridho Barakbah Politeknik Elektronika Negeri Surabaya
  • Achmad Basuki Politeknik Elektronika Negeri Surabaya

DOI:

https://doi.org/10.26594/register.v10i1.3501

Keywords:

Content-dependent image search, image retrieval, Feature aggregation, Automatic weighting

Abstract

The existing image search system extracts features from the database images and performs queries thoroughly without considering the weight of each feature. Currently, all features are assigned the same weight, even though each image has different characteristics. This study proposes a new approach to image search systems that relies on content with automatic weighting. The automatic weighting process starts by calculating each moment. The first moment is obtained from the color matrix and is calculated as the average value. The second moment is obtained from the texture matrix and is calculated as the variance value. The third moment is obtained from the shape matrix and is calculated as the skewness value. These three moments are normalized to give the same weight to each feature for each picture. The results obtained for accuracy were: 70.38% for color, 60.99% for shape, 71.21% for texture, 72.65% for color-shape combinations, 78.43% for color-texture combinations, 72.65% for texture-shape combinations, and 80.5% for overall texture-color-shape features.

References

M. I. Jaya, F. Sidi, I. Ishak, L. S. Affendey, and M. A. Jabar, "A review of data quality research in achieving high data quality within organization," J. Theor. Appl. Inf. Technol., vol. 95, no. 12, pp. 2647-2657, 2017.

G. Pennycook, Z. Epstein, M. Mosleh, A. A. Arechar, D. Eckles, and D. G. Rand, "Shifting attention to accuracy can reduce misinformation online," Nature, vol. 592, no. 7855, pp. 590-595, 2021, doi: 10.1038/s41586-021-03344-2.

A. . F. Adrakatti, R. S. Wodeyar, and K. R. Mulla, "Search by Image: A Novel Approach to Content Based Image Retrieval System," Int. J. Libr. Sci., vol. 14, no. 3, pp. 41-47, 2016, [Online]. Available: http://www.ceser.in/ceserp/index.php/ijls/article/view/4561

S. Joseph and O. O. Olugbara, "Detecting salient image objects using color histogram clustering for region granularity," J. Imaging, vol. 7, no. 9, 2021, doi: 10.3390/jimaging7090187.

F. Saba, M. J. Valadan Zoej, and M. Mokhtarzade, "Optimization of Multiresolution Segmentation for Object-Oriented Road Detection from High-Resolution Images," Can. J. Remote Sens., vol. 42, no. 2, pp. 75-84, 2016, doi: 10.1080/07038992.2016.1160770.

A. R. Barakbah and Y. Kiyoki, "Image Retrieval Systems with 3D-Color Vector Quantization and Cluster based Shape and Structure Features," Inf. Model. Knowl. Bases XXI, vol. 206, pp. 169-187, 2010.

A. Al-Mohamade, O. Bchir, and M. M. Ben Ismail, "Multiple query content-based image retrieval using relevance feature weight learning," J. Imaging, vol. 6, no. 1, 2020, doi: 10.3390/jimaging6010002.

F. H. D. Araujo et al., "Reverse image search for scientific data within and beyond the visible spectrum," Expert Syst. Appl., vol. 109, pp. 35-48, 2018, doi: 10.1016/j.eswa.2018.05.015.

I. M. Hameed, S. H. Abdulhussain, and B. M. Mahmmod, "Content-based image retrieval: A review of recent trends," Cogent Eng., vol. 8, no. 1, 2021, doi: 10.1080/23311916.2021.1927469.

A. Kumar, S. Choudhary, V. S. Khokhar, V. Meena, and C. Chattopadhyay, "Automatic Feature Weight Determination using Indexing and Pseudo-Relevance Feedback for Multi-feature Content-Based Image Retrieval," pp. 1-9, 2018, [Online]. Available: http://arxiv.org/abs/1812.04215

A. Latif et al., "Content-based image retrieval and feature extraction: A comprehensive review," Math. Probl. Eng., vol. 2019, 2019, doi: 10.1155/2019/9658350.

R. C. Winedhar, "Komputasi Budaya Untuk Pencarian Gambar Semantik Pada Lukisan Budaya Indonesia Dengan Deteksi Dan Informasi Aliran Lukisan," J. Teknol. Inf. dan Terap., vol. 8, no. 1, pp. 6-12, 2021, doi: 10.25047/jtit.v8i1.224.

A. A. Kurniasari, A. R. Barakbah, and A. Basuki, "Content-Dependent Image Search System for Aggregation of Color, Shape and Texture Features," Emit. Int. J. Eng. Technol., vol. 7, no. 1, pp. 223-242, 2019, doi: 10.24003/emitter.v7i1.361.

G. W. Jia Li James Ze Wang, "SIMPLIcity: Semantics-sensitive Integrated Matching for Picture LIbraries," IEEE Pami, vol. 23, no. 9, pp. 947-963, 2001, doi: 10.1109/34.955109.

M. Maleki, N. Manshouri, and T. Kayikcioglu, "A Novel Simple Method to Select Optimal k in k-Nearest Neighbor Classifier," vol. 15, no. 2, pp. 464-469, 2017.

A. R. Barakbah and K. Arai, "Determining Constraints of Moving Variance to Find Global Optimum and Make Automatic Clustering," Ind. Electron. Semin. 2004, pp. 409-413, 2004.

J. Qi, Y. Yu, L. Wang, J. Liu, and Y. Wang, "An effective and efficient hierarchical K-means clustering algorithm," Int. J. Distrib. Sens. Networks, vol. 13, no. 8, pp. 1-17, 2017, doi: 10.1177/1550147717728627.

A. R. Barakbah and Y. Kiyoki, "IMAGE RETRIEVAL SYSTEMS WITH 3D-COLOR VECTOR QUANTIZATION AND CLUSTER BASED SHAPE AND STRUCTURE FEATURE EXTRACTION System Design Color Feature Extraction Shape & Structure Feature Extraction," p. 1000.

A. R. Barakbah and Y. Kiyoki, "3D-Color Vector Quantization for Image Retrieval Systems," Int. Database Forum 2008, no. September 2008, pp. 13-18, 2008.

M. A. Sayeed, "Detecting Crows on Sowed Crop Fields using Simplistic Image processing Techniques by Open CV in comparison with TensorFlow Image Detection API," Int. J. Res. Appl. Sci. Eng. Technol., vol. 8, no. 3, pp. 61-73, 2020, doi: 10.22214/ijraset.2020.3014.

T. D. Pupitasari et al., "Intelligent detection of rice leaf diseases based on histogram color and closing morphological," Emirates J. Food Agric., vol. 34, no. 5, pp. 404-410, 2022, doi: 10.9755/ejfa.2022.v34.i5.2858.

H. Qazanfari, H. Hassanpour, and K. Qazanfari, "Content-Based Image Retrieval Using HSV Color Space Features," Int. J. Comput. Inf. Eng., vol. 13, no. 10, pp. 537-545, 2019.

L. He, X. Ren, Q. Gao, X. Zhao, B. Yao, and Y. Chao, "The connected-component labeling problem: A review of state-of-the-art algorithms," Pattern Recognit., vol. 70, pp. 25-43, 2017, doi: 10.1016/j.patcog.2017.04.018.

M. A. Ansari, D. Kurchaniya, and M. Dixit, "A Comprehensive Analysis of Image Edge Detection Techniques," Int. J. Multimed. Ubiquitous Eng., vol. 12, no. 11, pp. 1-12, 2017, doi: 10.14257/ijmue.2017.12.11.01.

S. Basheera and M. Satya Sai Ram, "Alzheimer’s disease classification using leung-malik filtered bank features and weak classifier," Int. J. Recent Technol. Eng., vol. 8, no. 3, pp. 1956-1961, 2019, doi: 10.35940/ijrte.C4484.098319.

E. S. Varnousfaderani, S. Yousefi, C. Bowd, A. Belghith, and M. H. Goldbaum, "Vessel Delineation in Retinal Images using Leung-Malik filters and Two Levels Hierarchical Learning," AMIA ... Annu. Symp. proceedings. AMIA Symp., vol. 2015, pp. 1140-1147, 2015.

A. Sengur, Y. Guo, M. Ustundag, and Ö. F. Alcin, "A Novel Edge Detection Algorithm Based on Texture Feature Coding," J. Intell. Syst., vol. 24, no. 2, pp. 235-248, 2015, doi: 10.1515/jisys-2014-0075.

A. R. Barakbah and Y. Kiyoki, "Image Search System with Automatic Weighting Mechanism for Selecting Features," 6th Int. Conf. Inf. Commun. Technol. Syst., 2010.

M. Faisal, E. M. Zamzami, and Sutarman, "Comparative Analysis of Inter-Centroid K-Means Performance using Euclidean Distance, Canberra Distance and Manhattan Distance," J. Phys. Conf. Ser., vol. 1566, no. 1, 2020, doi: 10.1088/1742-6596/1566/1/012112.

P. dwi Nurfadila, A. P. Wibawa, I. A. E. Zaeni, and A. Nafalski, "Journal Classification Using Cosine Similarity Method on Title and Abstract with Frequency-Based Stopword Removal ," Int. J. Artif. Intell. Res., vol. 3, no. 2, 2019, doi: 10.29099/ijair.v3i2.99.

Published

2024-04-27

How to Cite

[1]
A. Agus Kurniasari, Ali Ridho Barakbah, and Achmad Basuki, “Content-Dependent Image Search System with Automatic Weighting Mechanism for Aggregating Color, Shape, and Texture Features”, Register: Jurnal Ilmiah Teknologi Sistem Informasi, vol. 10, no. 1, Apr. 2024.

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