Abstract
The information changes in image pixel of retrieved records is very common in image process. The image content extraction is containing many parameters to reconstruct the image again for access the information. The intensity level, edge parameters are important parameter to reconstruct the image. The filtering techniques used to retrieve the image from query images. In this research article, the adaptive function kalman filter function performs for image retrieval to get better accuracy and high reliable compared to previous existing method includes Content Based Image Retrieval (CBIR). The kalman filter is incorporated with adaptive feature extraction for transition framework in the fine tuning of kalman gain. The feature vector database analysis provides transparent to choose the images in retrieval function from query images dataset for higher retrieval rate. The virtual connection is activated once in single process for improving reliability of the practice. Besides, this research article encompasses the adaptive updating prediction function in the estimation process. Our proposed framework construct with adaptive state transition Kalman filtering technique to improve retrieval rate. Finally, we achieved 96.2% of retrieval rate in the image retrieval process. We compare the performance measure such as accuracy, reliability and computation time of the process with existing methods.
References
Y. Wei, Y. Zhao, C. Lu et al., “Cross-modal retrieval with CNN visual features: a new baseline,” IEEE Transactions on Cybernetics, vol. 47, no. 2, pp. 449–460, 2017.
P. Liu, J.-M. Guo, C.-Y. Wu, and D. Cai, “Fusion of deep learning and compressed domain features for content-based image retrieval,” IEEE Transactions on Image Processing, vol. 26, no. 12, pp. 5706–5717, 2017.
S. Yu, D. Niu, L. Zhang, M. Liu, and X. Zhao, “Colour image retrieval based on the hypergraph combined with a weighted adjacent structure,” IET Computer Vision, vol. 12, no. 5, pp. 563–569, 2018.
B.-H. Yuan and G.-H. Liu, “Image retrieval based on gradientstructures histogram,” Neural Computing and Applications, 2019.
R. C. Gonzalez and R. E. Woods, Digital Image Processing, Prentice-Hall, Upper Saddle River, NJ, USA, 3rd edition, 2007.
D. H. Hubel and T. N. Wiesel, “Receptive fields of single neurones in the cat’s striate cortex,” %e Journal of Physiology, vol. 148, no. 3, pp. 574–591, 1959.
D. H. Hubel and T. N. Wiesel, “Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex,” %e Journal of Physiology, vol. 160, no. 1, pp. 106–154, 1962.
S. Zeng, R. Huang, H. Wang, and Z. Kang, “Image retrieval using spatiograms of colors quantized by Gaussian Mixture Models,” Neurocomputing, vol. 171, pp. 673–684, 2016.
A. Raza, H. Dawood, H. Dawood, S. Shabbir, R. Mehboob, and A. Banjar, “Correlated primary visual texton histogram features for content base image retrieval,” IEEE Access, vol. 6, pp. 46595–46616, 2018.
A. Raza, T. Nawaz, H. Dawood, and H. Dawood, “Square texton histogram features for image retrieval,” Multimedia Tools and Applications, vol. 78, no. 3, pp. 2719–2746, 2018.
M. Verma, B. Raman, and S. Murala, “Local extrema cooccurrence pattern for color and texture image retrieval,” Neurocomputing, vol. 165, pp. 255–269, 2015.
E. Walia, S. Vesal, and A. Pal, “An effective and fast hybrid framework for color image retrieval,” Sensing and Imaging, vol. 15, no. 1, p. 93, 2014.
J.-x. Zhou, X.-d. Liu, T.-w. Xu, J.-h. Gan, and W.-q. Liu, “A new fusion approach for content based image retrieval with color histogram and local directional pattern,” International Journal of Machine Learning and Cybernetics, vol. 9, no. 4, pp. 677–689, 2018.
S.-F. Chang, T. Sikora, and A. Purl, “Overview of the MPEG-7 standard,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 11, no. 6, pp. 688–695, 2001.
Sathesh et al “A Dual Tree Complex Wavelet Transform Construction and its application to Image Denoising” International Journal of Image Processing (IJIP) Volume(3), Issue(6).
Y. Ke and R. Sukthankar, “PCA-SIFT: A more distinctive representation for local image descriptors,” in Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004 (CVPR 2004), Washington, DC, USA, June 2004.
F. W. Campbell and J. J. Kulikowski, “Orientational selectivity of the human visual system,” %e Journal of Physiology, vol. 187, no. 2, pp. 437–445, 1966.
F. Liu, H. Duan, and Y. Deng, “A chaotic quantum-behaved particle swarm optimization based on lateral inhibition for image matching,” Optik, vol. 123, no. 21, pp. 1955–1960, 2012.
J. Huang, S. R. Kumar, M. Mitra, W.-J. Zhu, and R. Zabih, “Image indexing using color correlograms,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 762–768, San Juan, Puerto Rico, June 1997.
G. Pass, R. Zabih, and J. Miller, “Comparing images using color coherence vectors,” in Proceedings of the Fourth ACM International Conference on Multimedia-MULTIMEDIA’96, pp. 65–73, New York, NY, USA, June 1997.
W. Y. Ma and B. S. Manjunath, “A comparison of wavelet transform features for texture image annotation,” in Proceedings of the International Conference on Image Processing, vol. 2, pp. 256–259, Washington, DC, USA, October 1995.
W. Y. Kim and Y. S. Kim, “A region-based shape descriptor using Zernike moments,” Signal Processing: Image Communication, vol. 16, no. 1-2, pp. 95–102, 2000.
G.-H. Liu and J.-Y. Yang, “Content-based image retrieval using color difference histogram,” Pattern Recognition, vol. 46, no. 1, pp. 188–198, 2013.
A. Chadha and Y. Andreopoulos, “Voronoi-based compact image descriptors: efficient region-of-interest retrieval with VLAD and deep-learning-based descriptors,” IEEE Transactions on Multimedia, vol. 19, no. 7, pp. 1596–1608, 2017.
N. Shrivastava and V. Tyagi, “An efficient technique for retrieval of color images in large databases,” Computers & Electrical Engineering, vol. 46, pp. 314–327, 2015.
N. Varish, J. Pradhan, and A. K. Pal, “Image retrieval based on non-uniform bins of color histogram and dual tree complex wavelet transform,” Multimedia Tools and Applications, vol. 76, no. 14, pp. 15885–15921, 2017.
L. K. Pavithra and T. S. Sharmila, “An efficient framework for image retrieval using color, texture and edge features,” Computers & Electrical Engineering, vol. 70, pp. 580–593, 2018.
J. Ahmad, M. Sajjad, I. Mehmood, S. Rho, and S. W. Baik, “Saliency-weighted graphs for efficient visual content description and their applications in real-time image retrieval systems,” Journal of Real-Time Image Processing, vol. 13, no. 3, pp. 431–447, 2017.
J. Ahmad, M. Sajjad, I. Mehmood, and S. W. Baik, “SSH: salient structures histogram for content based image retrieval,” in Proceedings of the 2015 18th International Conference on Network-Based Information Systems (NBiS), pp. 212–217, Taipei, Taiwan, September 2015.
J. Pradhan, A. K. Pal, and H. Banka, “Principal texture direction-based block level image reordering and use of color edge features for application of object based image retrieval,” Multimedia Tools and Applications, vol. 78, no. 2, pp. 1685– 1717, 2018.
G.-H. Liu and J.-Y. Yang, “Exploiting color volume and color difference for salient region detection,” IEEE Transactions on Image Processing, vol. 28, no. 1, pp. 6–16, 2019.
G.-H. Liu, “Content-based image retrieval based on Cauchy density function histogram,” in Proceedings of the 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, pp. 506–510, Changsha, China, August 2016.
G.-H. Liu, “Content-based image retrieval based on visual attention and the conditional probability,” in Proceedings of the International Conference on Chemical, Material, and Food Engineering, pp. 838–842, Kunming, Yunnan, China, July 2015.
J.-Z. Hua, G.-H. Liu, and S.-X. Song, “Content-based image retrieval using color volume histograms,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 33, no. 9, Article ID 1940010, 2019.
J. Wu, L. Feng, S. Liu, and M. Sun, “Image retrieval framework based on texton uniform descriptor and modified manifold ranking,” Journal of Visual Communication and Image Representation, vol. 49, pp. 78–88, 2017.
S. Liu, J. Wu, L. Feng et al., “Perceptual uniform descriptor and ranking on manifold for image retrieval,” Information Sciences, vol. 424, pp. 235–249, 2018.
Herráez J. and Ferri J., “Combining Similarity Measures in Content-Based Image Retrieval,” Pattern Recognition Letters, vol. 29, no. 16, pp. 2174-2181, 2008.
S. G. Narasimhan and S. K. Nayar, “Interactive Deweathering of An Image Using Physical Model,” in IEEE Workshop on Color and Photometric Methods in Computer Vision, 2003.
Jayaprabha P. and Somasundaram M., “Content Based Image Retrieval Methods using Graphical Image Retrieval Algorithm,” Computer Science and Application, vol. 1, no. 1. pp. 9-14, 2012.
N. Hautiere and D. Aubert, “Contrast Restoration of Foggy Images through use of an Onboard Camera,” in Proc. IEEE Conf. Intelligent Transportation Systems, pp. 601–606, Sep. 2005.
T. Hiramatsu, T. Ogawa, and M. Haseyama, “A Kalman Filter based Restoration Method for In-vehicle Camera Images in Foggy Conditions,” in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP 2008), pp. 1245–1248, Apr. 2008.