access icon free Seed picking crossover optimisation algorithm for semantic segmentation from images

Semantic image segmentation treats the issues involved in the object recognition and image segmentation as a combined task. The chief notion of semantic segmentation is to partition the image into visually uniform regions and to discriminate the class of the partitioned regions. Pixel classification is done over the segmented regions by assigning semantic labels. In general, inference frameworks are fed with the combination of low-level features and high-level contextual cues to segment an image. Since these combinations are rarely object consistent, result with minimum classification accuracy because of choosing non-influencing features and cues to track specific objects. To overcome this problem, a nature-inspired meta-heuristic optimization algorithm called Seed Picking Crossover Optimization (SPCO) is proposed to optimize i.e. train the CRF (Conditional Random Field) for choosing relevant feature to segment the object with high accuracy. To meritoriously recognize the objects, a semi-segmentation process is initially performed using Simple Linear Iterative Clustering (SLIC) algorithm. For pixel transformation and pixel association, Dirichlet process mixture model and CRF are employed. Optimized CRFs are used where the parametric optimization is done using the proposed SPCO algorithm. The proposed work results with 84% on classification accuracy and the performance evaluations are done using MSRC-21 dataset.

Inspec keywords: learning (artificial intelligence); iterative methods; image segmentation; image representation; pattern clustering; object recognition; image classification; optimisation

Other keywords: object recognition; high-level contextual cues; Dirichlet process mixture model; pixel classification; minimum classification accuracy; low-level features; semisegmentation process; SPCO algorithm; conditional random field; parametric optimisation; CRF; semantic image segmentation; semantic labels; segmented regions; nature-inspired meta-heuristic optimisation algorithm; linear iterative clustering algorithm; simple linear iterative clustering algorithm; pixel transformation; optimised CRFs; MSRC-21 dataset; pixel association; partitioned regions; visually uniform regions; seed picking crossover optimisation algorithm

Subjects: Interpolation and function approximation (numerical analysis); Interpolation and function approximation (numerical analysis); Computer vision and image processing techniques; Knowledge engineering techniques; Optimisation techniques; Image recognition; Optimisation techniques

References

    1. 1)
      • 2. Yang Wang, X., Zhang, X., Yang, H.-Y.: ‘A pixel- based color image segmentation using support vector machine and fuzzy C-means’, Neural Netw., 2012, 33, pp. 148159.
    2. 2)
      • 22. Boix, X., Gonfaus, J.M., Weijer, J., et al: ‘Harmony potentials – fusing global and local scale for semantic image segmentation’, Int. J. Comput. Vis., 2012, 96, pp. 83102.
    3. 3)
      • 8. Bishaw, Z., Struik, P.C., Gastel, A.J. G.: ‘Farmers’ seed sources and seed quality: 1. Physical and physiological quality’, J. Crop Improv., 2012, 26, (5), pp. 655692.
    4. 4)
      • 18. Zand, M., Doraisamy, S., Halin, A.A., et al: ‘Ontology- based semantic image segmentation using mixture models and multiple CRFs’, IEEE Trans. Image Process., 2016, 25, (7), pp. 32333248.
    5. 5)
      • 10. Hettiarachchi, R., Petersab, J.F.: ‘Voronoï region-based adaptive unsupervised color image segmentation’, Pattern Recognit., 2017, 65, pp. 19135.
    6. 6)
      • 6. Zhang, H., Jonathan Wu, Q.M, Nguyen, T.M.: ‘Image segmentation by Dirichlet process mixture model with generalized mean’, IET Image Process., 2014, 8, (2), pp. 103111.
    7. 7)
      • 23. Zhu, L., Chen, Y., Lin, Y., et al: ‘Recursive segmentation and recognition templates for image parsing’, IEEE Trans. Pattern Anal. Mach. Intell., 2012, 34, (2), pp. 359371.
    8. 8)
      • 21. Simon, D.: ‘Biogeography-based optimization’, IEEE Trans. Evol. Comput., 2008, 12, pp. 702713.
    9. 9)
      • 12. Zhang, L., Ji, Q.: ‘Image segmentation with a unified graphical model’, IEEE Trans. Pattern Anal. Mach. Intell., 2010, 32, (8), pp. 4061425.
    10. 10)
      • 3. Li, W.: ‘Object recognition based on the region of interest and optimal bag of words model’, Neurocomputing, 2016, 172, pp. 271280.
    11. 11)
      • 11. Ding, L., Yilmaz, A., Yan, R.: ‘Interactive image segmentation using Dirichlet process multiple-view learning’, IEEE Trans. Image Process., 2012, 21, (4), pp. 21192129.
    12. 12)
      • 4. Song, D., Liu, W., Zhou, T., et al: ‘Efficient robust conditional random fields’, IEEE Trans. Image Process., 2015, 24, (10), pp. 31243136.
    13. 13)
      • 14. Peng, Z., Li, Q.: ‘Adaptive appearance separation for interactive image segmentation based on dense CRF’, IET Image Process., 2019, 13, (1), pp. 142151.
    14. 14)
      • 9. Hudwenz, A., Pufal, G., Bogeholz, A-L., et al: ‘Cross- pollination benefits differ among oilseed rape varieties’, J. Agric. Sci., 2014, 152, (5), pp. 770778.
    15. 15)
      • 13. Noormohamadi, N., Adibi, P., Ehsani, S.M.S.: ‘Semantic image segmentation using an improved hierarchical graphical model’, IET Image Process., 2018, 12, (11), pp. 19431950.
    16. 16)
      • 15. Jain, M., Singh, V., Rani, A.: ‘A novel nature-inspired algorithm for optimization: squirrel search algorithm’, Swarm Evol. Comput., 2019, 44, pp. 148175.
    17. 17)
      • 20. Maruyama, T., Igarashi, H.: ‘An effective robust optimization based on genetic algorithm’, IEEE Trans. Magnetics, 2008, 44, (6), pp. 990993.
    18. 18)
      • 5. Achanta, R., Shaji, A., Smith, K., et al: ‘SLIC superpixels compared to state-of-the-art superpixel methods’, IEEE Trans. Pattern Anal. Mach. Intell., 2012, 34, (11), pp. 22742282.
    19. 19)
      • 1. Dam, E.B, Loog, M.: ‘Efficient segmentation by sparse pixel classification’, IEEE Trans. Med. Imaging, 2008, 27, (10), pp. 15251534.
    20. 20)
      • 7. Oliveira, V., Ferreira, M.A.R.: ‘Maximum likelihood and restricted maximum likelihood estimation for a class of Gaussian Markov random fieldsvol. 74 (Springer, Metrika, 2011), pp. 167183.
    21. 21)
      • 19. Mirjalili, S., Andrew, L.: ‘The whale optimization algorithm’, Adv. Eng. Softw., 2016, 95, pp. 5167.
    22. 22)
      • 16. Kennedy, J., Eberhart, R.: ‘Particle swarm optimization’. Proc. Int. Conf. on Neural Networks, Perth, WA, Australia, 1995, pp. 19421948.
    23. 23)
      • 24. Zhou, C., Liu, C.: ‘Semantic image segmentation using low-level features and contextual cues’, Comput. Electr. Eng., 2014, 40, (3), pp. 844857.
    24. 24)
      • 17. Yuanjing, F., Li, Y., Liangjun, K..: ‘Finite grade pheromone ant colony optimization for image segmentation’, Opto-Electron. Rev., 2008, 16, (2), pp. 163171.
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