Integrating CNN and KNN for Enhanced Image Content Algorithm
Keywords:
CBIR, CNN, QBIC, Hybrid Algorithm, Image retrieval, KNN, Machine LearningAbstract
Multimedia content analysis is critical for visual data processing, particularly given the prevalence of digital visuals. Retrieving appropriate photos from networks such as Twitter and Instagram is a difficult research problem in computer vision. Traditional text-based search engines are inadequate to handle the volume and variety of visual data. To solve this, we present a novel hybrid CBIR model that combines CNN and KNN. This methodology enhances retrieval efficiency by closing the gap between picture attributes and human visual perception. We investigate a variety of retrieval techniques, including standard feature extraction and deep learning approaches. Our hybrid model for Query by Image Content Retrieval combines CNN feature extraction with KNN classification. By combining the two, we improve the effectiveness and efficiency of CBIR.
Downloads
References
D. Zhang, M. M. Islam, and G. Lu, “A review on automatic image annotation techniques,” Pattern Recognit., vol. 45, no. 1, pp. 346–362, 2012.
Z. Yu and W. Wang, “Learning DALTS for cross‐modal retrieval,” CAAI Trans. Intell. Technol., vol. 4, no. 1, pp. 9–16, 2019.
N. Ali, D. A. Mazhar, Z. Iqbal, R. Ashraf, J. Ahmed, and F. Z. Khan, “Content-based image retrieval based on late fusion of binary and local descriptors,” arXiv Prepr. arXiv1703.08492, 2017.
B. Zafar et al., “Intelligent image classification-based on spatial weighted histograms of concentric circles,” Comput. Sci. Inf. Syst., vol. 15, no. 3, pp. 615–633, 2018.
N. Ali, “Image Retrieval Using Visual Image Features and Automatic Image Annotation.” University of Engineering and Technology, Taxila, Pakistan., 2016.
L. Piras and G. Giacinto, “Information fusion in content based image retrieval: A comprehensive overview,” Inf. Fusion, vol. 37, pp. 50–60, 2017.
Y. Liu, D. Zhang, G. Lu, and W.-Y. Ma, “A survey of content-based image retrieval with high-level semantics,” Pattern Recognit., vol. 40, no. 1, pp. 262–282, 2007.
G. Qi, H. Wang, M. Haner, C. Weng, S. Chen, and Z. Zhu, “Convolutional neural network based detection and judgement of environmental obstacle in vehicle operation,” CAAI Trans. Intell. Technol., vol. 4, no. 2, pp. 80–91, 2019.
U. Markowska-Kaczmar and H. Kwaśnicka, “Deep learning—A new era in bridging the semantic gap,” Bridg. Semant. Gap Image Video Anal., pp. 123–159, 2018.
T. Khalil, M. U. Akram, H. Raja, A. Jameel, and I. Basit, “Detection of glaucoma using cup to disc ratio from spectral domain optical coherence tomography images,” IEEE Access, vol. 6, pp. 4560–4576, 2018.
H. Shao, Y. Wu, W. Cui, and J. Zhang, “Image retrieval based on MPEG-7 dominant color descriptor,” in 2008 The 9th international conference for young computer scientists, 2008, pp. 753–757.
X. Duanmu, “Image retrieval using color moment invariant,” in 2010 Seventh international conference on information technology: new generations, 2010, pp. 200–203.
X.-Y. Wang, B.-B. Zhang, and H.-Y. Yang, “Content-based image retrieval by integrating color and texture features,” Multimed. Tools Appl., vol. 68, no. 3, pp. 545–569, 2014.
H. Zhang, Z. Dong, and H. Shu, “Object recognition by a complete set of pseudo-Zernike moment invariants,” in 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010, pp. 930–933.
G. A. Papakostas, D. E. Koulouriotis, and V. D. Tourassis, “Feature extraction based on wavelet moments and moment invariants in machine vision systems,” Human-centric Mach. Vis., p. 31, 2012.
G.-H. Liu, Z.-Y. Li, L. Zhang, and Y. Xu, “Image retrieval based on micro-structure descriptor,” Pattern Recognit., vol. 44, no. 9, pp. 2123–2133, 2011.
X. Wang, Z. Chen, and J. Yun, “An effective method for color image retrieval based on texture,” Comput. Stand. Interfaces, vol. 34, no. 1, pp. 31–35, 2012.
D. Zhang and G. Lu, “Review of shape representation and description techniques,” Pattern Recognit., vol. 37, no. 1, pp. 1–19, 2004.
L. Amelio, R. Janković, and A. Amelio, “A new dissimilarity measure for clustering with application to dermoscopic images,” in 2018 9th international conference on information, intelligence, systems and applications (IISA), 2018, pp. 1–8.
N. Ali et al., “A novel image retrieval based on visual words integration of SIFT and SURF,” PLoS One, vol. 11, no. 6, pp. 1–20, 2016.
S. Lazebnik, C. Schmid, and J. Ponce, “Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2, pp. 2169–2178, 2006.
Z. Mehmood, S. M. Anwar, N. Ali, H. A. Habib, and M. Rashid, “A Novel Image Retrieval Based on a Combination of Local and Global Histograms of Visual Words,” Math. Probl. Eng., vol. 2016, 2016.
M. Naeem, R. Ashraf, N. Ali, M. Ahmad, and M. A. Habib, “Bottom up approach for better requirements elicitation,” ACM Int. Conf. Proceeding Ser., vol. Part F1305, pp. 17–20, 2017.
B. Zafar et al., “A novel discriminating and relative global spatial image representation with applications in CBIR,” Appl. Sci., vol. 8, no. 11, p. 2242, 2018.
N. Ali et al., “A hybrid geometric spatial image representation for scene classification,” PLoS One, vol. 13, no. 9, p. e0203339, 2018.
B. Zafar, R. Ashraf, N. Ali, M. Ahmed, S. Jabbar, and S. A. Chatzichristofis, “Image classification by addition of spatial information based on histograms of orthogonal vectors,” PLoS One, vol. 13, no. 6, p. e0198175, 2018.
N. Ali, K. B. Bajwa, R. Sablatnig, and Z. Mehmood, “Image retrieval by addition of spatial information based on histograms of triangular regions,” Comput. Electr. Eng., vol. 54, pp. 539–550, 2016.
L.-W. Kang, C.-Y. Hsu, H.-W. Chen, C.-S. Lu, C.-Y. Lin, and S.-C. Pei, “Feature-based sparse representation for image similarity assessment,” IEEE Trans. Multimed., vol. 13, no. 5, pp. 1019–1030, 2011.
Z.-Q. Zhao, H. Glotin, Z. Xie, J. Gao, and X. Wu, “Cooperative sparse representation in two opposite directions for semi-supervised image annotation,” IEEE Trans. Image Process., vol. 21, no. 9, pp. 4218–4231, 2012.
X. Shi, M. Sapkota, F. Xing, F. Liu, L. Cui, and L. Yang, “Pairwise based deep ranking hashing for histopathology image classification and retrieval,” Pattern Recognit., vol. 81, pp. 14–22, 2018.
L. Zhu, J. Shen, L. Xie, and Z. Cheng, “Unsupervised visual hashing with semantic assistant for content-based image retrieval,” IEEE Trans. Knowl. Data Eng., vol. 29, no. 2, pp. 472–486, 2016.
G. Kaur and S. Saini, “Comparison of State Vector Machine and Decision Tree-Content Based Image Retrieval Algorithms to Perceive Accuracy,” in 2023 1st International Conference on Innovations in High Speed Communication and Signal Processing (IHCSP), 2023, pp. 11–15.
G. Kaur, S. Saini, and A. Sehgal, “Introduction to Artificial Intelligence,” in Artificial Intelligence, Chapman and Hall/CRC, 2022, pp. 1–20.
G. Kaur, S. Saini, and A. Sehgal, “Machine Learning–Principles and Algorithms,” in Artificial Intelligence, Chapman and Hall/CRC, 2022, pp. 21–54.
G. Kaur, S. Saini, and A. Sehgal, “Applications of Machine Learning and Deep Learning,” in Artificial Intelligence, Chapman and Hall/CRC, 2022, pp. 55–70.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
