91
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Neuromorphic computing spiking neural network edge detection model for content based image retrieval

&
Received 08 Nov 2023, Accepted 22 Apr 2024, Published online: 06 May 2024

References

  • Ahmed KT, Ummesafi S, Iqbal A. 2019. Content based image retrieval using image features information fusion. Inf Fusion. 51:76–99. doi: 10.1016/j.inffus.2018.11.004.
  • Ashraf R, Ahmed M, Jabbar S, Khalid S, Ahmad A, Din S, Jeon G. 2018. Content based image retrieval by using color descriptor and discrete wavelet transform. J Med Syst. 42(3):1–12. doi: 10.1007/s10916-017-0880-7.
  • Bahrami MK, Nazari S. 2024. Digital design of a spatial-pow-STDP learning block with high accuracy utilizing pow CORDIC for large-scale image classifier spatiotemporal SNN. Sci Rep. 14(1):3388. doi: 10.1038/s41598-024-54043-7.
  • Bella MIT, Vasuki A. 2019. An efficient image retrieval framework using fused information feature. Comput Electr Eng. 75:46–60. doi: 10.1016/j.compeleceng.2019.01.022.
  • Bhunia AK, Bhattacharyya A, Banerjee P, Roy PP, Murala S. 2020. A novel feature descriptor for image retrieval by combining modified color histogram and diagonally symmetric co-occurrence texture pattern. Pattern Anal Appl. 23(2):703–723. doi: 10.1007/s10044-019-00827-x.
  • Buhmann JM, Lange T, Ramacher U. 2005. Image segmentation by networks of spiking neurons. Neural Comput. 17(5):1010–1031. doi: 10.1162/0899766053491913.
  • Canny J. 1986. A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell. PAMI-8(6):679–698. doi: 10.1109/TPAMI.1986.4767851.
  • Chandwadkar R, Dhole S, Gadewar V, Raut D, Tiwaskar SA 2013. Comparison of edge detection techniques. Annual Conference of IRAJ; October 6; Pune, India.
  • Chen Y-H, Chang C-C, Hsu C-Y. 2020. Content-based image retrieval using block truncation coding based on edge quantization. Conn Sci. 32(4):431–448. doi: 10.1080/09540091.2020.1753174.
  • Chen J, Qiu X, Ding C, Wu Y. 2022. SAR image classification based on spiking neural network through spike-time dependent plasticity and gradient descent. ISPRS J Photogramm. 188:109–124. doi: 10.1016/j.isprsjprs.2022.03.021.
  • Clogenson M, Kerr D, TM M, Coleman SA, Wu Q. 2011. Biologically inspired edge detection using spiking neural networks and hexagonal images. International Conference on Neural Computation Theory and Applications; Jan 1; Paris, France. SciTePress. p. 381–384. doi: 10.5220/0003682103810384.
  • Destexhe A. 1997. Conductance-based integrate-and-fire models. Neural Comput. 9(3):503–514. doi: 10.1162/neco.1997.9.3.503.
  • Dey M, Raman B, Verma M. 2016. A novel colour-and texture-based image retrieval technique using multi-resolution local extrema peak valley pattern and RGB colour histogram. Pattern Anal Appl. 19(4):1159–1179. doi: 10.1007/s10044-015-0522-y.
  • Fadaei S. 2022. New dominant color descriptor features based on weighting of more informative pixels using suitable masks for content-based image retrieval. Int J Eng. 35(8):1457–1467. doi: 10.5829/IJE.2022.35.08B.01.
  • Fadaei S, Dehghani A, Ravaei B. 2024. Content-based image retrieval using multi-scale averaging local binary patterns. Digit Signal Process. 146:104391. doi: 10.1016/j.dsp.2024.104391.
  • Feng S, Xu D, Yang X. 2010. Attention-driven salient edge (s) and region (s) extraction with application to CBIR. Signal Process. 90(1):1–15. doi: 10.1016/j.sigpro.2009.05.017.
  • Flickner M, Sawhney H, Niblack W, Ashley J, Huang Q, Dom B, Gorkani M, Hafner J, Lee D, Petkovic D, et al. 1995. Query by image and video content. The QBIC System Computer. 28(9):23–32. doi: 10.1109/2.410146.
  • Ghosh-Dastidar S, Adeli H. 2009. Spiking neural networks. Int J Neural Syst. 19(4):295–308. doi: 10.1142/S0129065709002002.
  • Griffin G, Holub A, Perona P. 2007. Caltech-256 Object Category Dataset, Technical Report. Pasadena: California Institute of Technology. 7694.
  • Haralick RM, Shanmugam K, Dinstein IH. 1973. Textural features for image classification. IEEE Trans Syst Man Cybern. SMC-3(6):610–621. doi: 10.1109/TSMC.1973.4309314.
  • Hodgkin AL, Huxley AF. 1952. A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol. 117(4):500. doi: 10.1113/jphysiol.1952.sp004764.
  • Hua J-Z, Liu G-H, Song S-X. 2019. Content-based image retrieval using color volume histograms. Intern J Pattern Recognit Artif Intell. 33(11):1940010. doi: 10.1142/S021800141940010X.
  • İncetaş MO. 2022. Anisotropic diffusion filter based on spiking neural network model. Arab J Sci Eng. 47(8):9849–9860. doi: 10.1007/s13369-021-06404-x.
  • İncetaş MO. 2023. Image interpolation based on spiking neural network model. Appl Sci. 13(4):2438. doi: 10.3390/app13042438.
  • İNCETAŞ MO, Zhang T, Lyu Y, Lai S, Dai P, Zheng J, Yang W, Zhou X-H, Feng L. 2023. Influenza’s plummeting during the COVID-19 pandemic: the roles of mask-wearing, mobility change, and SARS-CoV-2 interference. Curr Res Eng. 21:195–202. doi: 10.1016/j.eng.2021.12.011.
  • Izhikevich EM. 2003. Simple model of spiking neurons. IEEE Trans Neural Netw. 14(6):1569–1572. doi: 10.1109/TNN.2003.820440.
  • Izhikevich EM, FitzHugh R. 2006. Fitzhugh-nagumo model. Scholarpedia. 1(9):1349. doi: 10.4249/scholarpedia.1349.
  • Jiang D, Kim J. 2021. Image retrieval method based on image feature fusion and discrete cosine transform. Appl Sci. 11(12):5701. doi: 10.3390/app11125701.
  • Kanaparthi SK, Raju U, Shanmukhi P, Aneesha GK, Rahman MEU. 2020. Image retrieval by integrating global correlation of color and intensity histograms with local texture features. Multimedia Tools Appl. 79(47–48):34875–34911. doi: 10.1007/s11042-019-08029-7.
  • Kayhan N, Fekri-Ershad S. 2021. Content based image retrieval based on weighted fusion of texture and color features derived from modified local binary patterns and local neighborhood difference patterns. Multimedia Tools Appl. 80(21–23):32763–32790. doi: 10.1007/s11042-021-11217-z.
  • Keil MS, Cristobal G, Neumann H. 2006. Gradient representation and perception in the early visual system—a novel account of mach band formation. Vision Res. 46(17):2659–2674. doi: 10.1016/j.visres.2006.01.038.
  • Kerr D, TM M, Coleman S, Clogenson M. 2015. A biologically inspired spiking model of visual processing for image feature detection. Neurocomputing. 158:268–280. doi: 10.1016/j.neucom.2015.01.011.
  • Kerr D, TM M, Coleman SA, Wu Q, Clogenson M 2011. Spiking hierarchical neural network for corner detection. International Conference on Neural Computation Theory and Applications. SciTePress; Jan 1; Paris, France. p. 230–235. doi: 10.5220/0003682402300235.
  • Kilicaslan M, Tanyeri U, Demirci R. 2020. Image retrieval using one-dimensional color histogram created with entropy. Adv Electr Comput En. 20(2):79–88. doi: 10.4316/AECE.2020.02010.
  • Lee H, Kim C, Lee S, Baek E, Kim J. 2021. An accurate and fair evaluation methodology for SNN-based inferencing with full-stack hardware design space explorations. Neurocomputing. 455:125–138. doi: 10.1016/j.neucom.2021.05.020.
  • Liu G-H, Yang J-Y. 2013. Content-based image retrieval using color difference histogram. Pattern Recogn. 46(1):188–198. doi: 10.1016/j.patcog.2012.06.001.
  • Lowe DG. 2004. Distinctive image features from scale-invariant keypoints. Int J Comput Vision. 60(2):91–110. doi: 10.1023/B:VISI.0000029664.99615.94.
  • Manjunath B, Chellappa R. 1993. A unified approach to boundary perception: edges, textures, and illusory contours. IEEE Trans Neural Netw. 4(1):96–108. doi: 10.1109/72.182699.
  • Meng M, Yang X, Bi L, Kim J, Xiao S, Yu Z. 2021. High-parallelism inception-like spiking neural networks for unsupervised feature learning. Neurocomputing. 441:92–104. doi: 10.1016/j.neucom.2021.02.027.
  • Murala S, Maheshwari R, Balasubramanian R. 2012. Directional local extrema patterns: a new descriptor for content based image retrieval. Int J Multimed Inf Retr. 1(3):191–203. doi: 10.1007/s13735-012-0008-2.
  • Natschläger T, Ruf B. 1998. Spatial and temporal pattern analysis via spiking neurons. Network: Comput Neural Syst. 9(3):319. doi: 10.1088/0954-898X_9_3_003.
  • Nazir A, Ashraf R, Hamdani T, Ali N 2018. Content based image retrieval system by using HSV color histogram, discrete wavelet transform and edge histogram descriptor. IEEE International conference on computing, mathematics and engineering technologies; Mar 03-04; Sukkur, Pakistan. IEEE. p. 1–6. doi: 10.1109/ICOMET.2018.8346343.
  • Ojala T, Pietikainen M, Maenpaa T. 2002. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE T Pattern Anal. 24(7):971–987. doi: 10.1109/TPAMI.2002.1017623.
  • Oliva A, Torralba A. 2001. Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vision. 42(3):145–175. doi: 10.1023/A:1011139631724.
  • Palconit MG, Conception IR, Alejandrino JD, Evangelista IR, Sybingco E, Vicerra RR, Bandala AA, Dadios EP2021. Counting of uneaten floating feed pellets in water surface images using ACF detector and Sobel edge operator. IEEE 9th Region 10 Humanitarian Technology Conference; 30 Sept - 02 Oct; Bangalore, India. IEEE. p. 1–5. doi: 10.1109/R10-HTC53172.2021.9641579.
  • Parhi KK, Unnikrishnan NK. 2020. Brain-inspired computing: models and architectures. IEEE Open J Circui And Syst. 1:185–204. doi: 10.1109/OJCAS.2020.3032092.
  • Pentland A, Picard R, Scarloff S. 1994. Photobook: tools for content-based manipulation of image databases. Proc. SPIE 2368, 23rd AIPR Workshop: Image and Information Systems: Applications and Opportunities; Jan 31; Washington, DC, United States. p. 37–50. doi: 10.1117/12.200805.
  • Plataniotis K, Venetsanopoulos AN. 2000. Color image processing and applications. 1st ed. Berlin, Heidelberg, Germany: Springer Science & Business Media. p. XX, 355. doi: 10.1007/978-3-662-04186-4.
  • Pratt WK. 2007. Digital image processing: PIKS scientific inside. Vol. 4. Hoboken, New Jersey: Wiley Online Library. doi: 10.1002/0470097434.
  • Qu P, Lin H, Pang M, Liu X, Zheng W, Zhang Y. 2023. ENLARGE: an efficient SNN Simulation Framework on GPU clusters. IEEE Trans on Parallel & Distributed Systems. 34:2529–2540. doi: 10.1109/TPDS.2023.3291825.
  • Raja R, Kumar S, Mahmood MR. 2020. Color object detection based image retrieval using ROI segmentation with multi-feature method. Wirel Pers Commun. 112(1):169–192. doi: 10.1007/s11277-019-07021-6.
  • Raza A, Nawaz T, Dawood H, Dawood H. 2019. Square texton histogram features for image retrieval. Multimedia Tools Appl. 78(3):2719–2746. doi: 10.1007/s11042-018-5795-x.
  • Shih J-L, Chen L-H. 2002. Colour image retrieval based on primitives of colour moments. IEE Proc - Vision, Image and Signal Process. 149 (6):370–376. doi: 10.1049/ip-vis:20020614.
  • Singhal A, Agarwal M, Pachori RB. 2021. Directional local ternary co-occurrence pattern for natural image retrieval. Multimedia Tools Appl. 80(10):15901–15920. doi: 10.1007/s11042-020-10319-4.
  • Singh C, Walia E, Kaur KP. 2018. Color texture description with novel local binary patterns for effective image retrieval. Pattern Recogn. 76:50–68. doi: 10.1016/j.patcog.2017.10.021.
  • Smith JR, Chang SF 1997. VisualSEEk: a fully automated content-based image query system. Proceedings of the fourth ACM international conference on Multimedia; Nov 20; Boston, MA. New York, NY, USA: Association for Computing Machinery. p. 87–98. doi: 10.1145/244130.244151.
  • Stricker M, Orengo M. 1995. Similarity of color images. Storage and Retrieval for Image and Video Databases III; March 23; San Jose, CA, United States. Science and Technology; p. 381–392. doi: 10.1117/12.205308.
  • Sudars K, Jasko J, Namatevs I, Ozola L, Badaukis N. 2020. Dataset of annotated food crops and weed images for robotic computer vision control. Data Brief. 31:105833. doi: 10.1016/j.dib.2020.105833.
  • Swain MJ, Ballard DH. 1991. Color indexing. Int J Comput Vision. 7(1):11–32. doi: 10.1007/BF00130487.
  • Vemuru KV. 2020. Image edge detector with Gabor type filters using a spiking neural network of biologically inspired neurons. Algorithms. 13(7):165. doi: 10.3390/a13070165.
  • Verma M, Raman B, Murala S. 2015. Local extrema co-occurrence pattern for color and texture image retrieval. Neurocomputing. 165:255–269. doi: 10.1016/j.neucom.2015.03.015.
  • Vicente-Sola A, Manna DL, Kirkland P, Di Caterina G, Bihl T. 2022. Keys to accurate feature extraction using residual spiking neural networks. Neuromorp Comput And Enginee. 2(4):044001. doi: 10.1088/2634-4386/ac8bef.
  • Wang JZ, Li J, Wiederhold G. 2001. Simplicity: semantics-sensitive integrated matching for picture libraries. IEEE T Pattern Anal. 23(9):947–963. doi: 10.1109/34.955109.
  • Wu Q, McGinnity M, Maguire L, Belatreche A, Glackin B 2007. Edge detection based on spiking neural network model. Advanced Intelligent Computing Theories and Applications With Aspects of Artificial Intelligence: Third International Conference on Intelligent Computing. Springer Berlin Heidelberg; 3: p.26–34.
  • Wu Q, TM M, Maguire L, Cai R, Chen M. 2013. A visual attention model based on hierarchical spiking neural networks. Neurocomputing. 116:3–12. doi: 10.1016/j.neucom.2012.01.046.
  • Wu F, Zhu C, Xu J, Bhatt MW, Sharma A 2021. Research on image text recognition based on canny edge detection algorithm and k-means algorithm. Intern J Syst Assur Enginee Manage. 1–9.
  • Yedjour H, Meftah B, Lézoray O, Benyettou A. 2017. Edge detection based on Hodgkin–Huxley neuron model simulation. Cognit Process. 18(3):315–323. doi: 10.1007/s10339-017-0803-z.
  • Yuan B-H, Liu G-H. 2020. Image retrieval based on gradient-structures histogram. Neural Comput Appl. 32(15):11717–11727. doi: 10.1007/s00521-019-04657-0.
  • Zhang Y, Xue L, Qu H 2023. A bionic spiking recurrent neural network with sparse connections and Dale’s principle. 4538536.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.