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An Efficient Image Retrieval Scheme for Sketches using Fish Swarm Optimization with the Aid of Optimal Score Level Fusion

Affiliations

  • GITAM University, Visakhapatnam - 530045, Andhra Pradesh, India
  • ANU College of Engineering, Guntur - 522510, Andhra Pradesh, India

Abstract


Objectives: Image retrieval is system software for browsing, examining and retrieving images from a large database of images. Images and sketches do not share numerous common modalities. Hence, Sketch to Image Retrieval is a tedious task in image processing. Sketch image retrieval focuses on the hand-drawn query and retrieves the similar images from a large database which is useful for further processing.Methods/Statistical Analysis:In the, most of the traditional/conventional image processing techniques considered edges and outlines for image retrieval. In this paper, a new methodology is developed by fusion of Edge Histogram Descriptors, Histogram of oriented gradients, Scale Invariant Feature Transform (SIFT) and Speeded up Robust Features (SURF). In the proposed model first feature Extraction is carried out and Euclidian distance is calculated amongst the query sketch and innovative image. Formerly the feature vectors are provided to the score level fusion stage, and then they obtained results are optimized. For optimization, Fish Swarm Optimization (FSO) is employed in the proposed method. Findings: The performance of the proposed method is evaluated through Benchmark sketch image database. Also, the attained results are compared with the existing evolutionary algorithm Genetic Algorithm (GA). Application/ Improvement: The experimental results showed that the projected method with FSO yields better results than GA.

Keywords

EHD, Fish Swarm Optimization, Genetic Algorithm, HOG, Score Level Fusion,SIFT, SURF.

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References


  • Jose SM, Bustos B. Sketch-based image retrieval using key shapes in Springer link. Multimedia Tools and Applications. 2014; 73(3):2033–62.
  • Li L, Yi Y, Hospedales TH, Song Y-Z, Gong S. Fine-grained sketch-based image retrieval by matching deformable part models. Neurocomputing, London; 2014. p. 1–12.
  • Parui P, Sarthak S, Mittal A. Similarity-invariant sketch-based image retrieval in large databases. Computer Vision – ECCV 2014; 2014. p. 398–414.
  • Reddy NRR, Reddy GS, Narayana M. Color sketch based image retrieval. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering. 2014; 3(9):12179–85.
  • Bozas B, Konstantinos K, Izquierdo E. Large scale sketch based image retrieval using patch hashing. Advances in Visual Computing; 2012. p. 210–19.
  • Sundaresan SM, Srinivasagan KG. Design of image retrieval efficacy system based on CBIR. International Journal of Advanced Research in Computer Science and Software Engineering. 2013; 3(4):48–53.
  • Patwal PP, Srivastava AK. A content-based indexing system for image. Indian Journal of Science and Technology. 2016; 9(29):1–7.
  • Prajapati N, Prajapti GS. Sketch based image retrieval system for the web - a survey. International Journal of Computer Science and Information Technologies. 2015; 6(4):3973–9 .
  • Filho CAF, De A, Arajujo A, Crucianu M, Gouet-Brunet VB. Sketch-finder: Efficient and effective sketch-based retrieval for large image collections. Proceedings of IEEE International Conference on Graphics, Patterns and Images, Brazil; 2013. p. 234–41.
  • Kumara YHS, Gurub DS. Retrieval of flower based on sketches. Procedia Computer Science. 2015; 46:1577–84.
  • Wang X, Duan X, Bai X. Deep sketch feature for cross-domain image retrieval. Journal of Neurocomputing. 2016; 207(6):1–24.
  • Szántó BP, Pozsegovics Z, Vámossy V, Sergyán SZ. Sketch4match-content-based image retrieval system using sketches. Proceedings of IEEE International Symposium on Applied Machine Intelligence and Informatics (SAMI), Hungary; 2011. p. 183–8.
  • Liu L, Ching-Hsuan C, Lin Y L, Cheng WF, Winston H, Hsu H. Exploiting word and visual word co-occurrence for sketch-based clipart image. Proceedings of ACM Conference on Multimedia Conference, USA; 2015. p. 867–70.
  • Gaidhani G, Prachi A, Bagal SB. Survey paper on sketch based and content based image. International Journal of Science and Research. 2015; 4(12):1–7.
  • Pawar S, Tidke S. Survey on sketch based image retrieval system. International Journal of Emerging Technology and Advanced Engineering. 2014; 4(8):418–23.
  • Wang W, Shu S, Zhang J, Tony X, Han H, Miao Z. Sketch-based image retrieval through hypothesis-driven object boundary selection with HLR descriptor. IEEE Transactions on Multimedia. 2015; 17(7):1045–57.
  • Qian Q, Xueming X, Tan X, Zhang Y, Hong R, Wang M. Enhancing sketch-based image retrieval by re-ranking and relevance feedback. IEEE Transactions on Image Processing. 2016; 25(1):195–208.
  • Ma M, Chao C, Yang X, Zhang C, Ruan X, Yang MH. Sketch retrieval via dense stroke features. Image and Vision Computing. 2016; 46(1):64–73.
  • Rao K, Mallikharjuna M, Kodali K, Anuradha A. An efficient method for parameter estimation of software reliability growth model using artificial bee colony optimization. Proceedings of 5th International Conference on SEMCCO- Lecture Notes in Computer Science, Springer Series; 2015. p. 765–76.
  • Park DK, Jeon YS, Won CS. Efficient use of local edge histogram descriptor. MULTIMEDIA ‘00 Proceedings of the 2000 ACM Workshops on Multimedia; 2000. p. 51–4.
  • Hu H, Rui R, Collomosse J. A performance evaluation of gradient field hog descriptor for sketch based image retrieval. Computer Vision and Image Understanding. 2013; 117(7):790–806.
  • Sreeja M, Anees VM, Kumar GS. Automatic image annotation using SURF descriptors India Conference (INDICON); 2012.
  • Jhansi Y, Reddy ES. A methodology for sketch based image retrieval based on score level fusion. International Journal of Computer Applications. 2015; 109(3):9–13.
  • Azizi R. Empirical study of artificial fish swarm algorithm. International Journal of Computing, Communications and Networking. 2014; 3(1):1–5.
  • Eitz M, Hildebrand K, Boubekeur T, Alexa M. sketch-based image retrieval: benchmark and bag-of-features descriptors . IEEE Transactions on Visualization and Computer Graphics. 2011; 17(11):1624–36.
  • Arora M, Kanjilal U, Varshney D. Evaluation of information retrieval: Precision and recall. International Journal of Indian Culture and Business Management. 2016; 12(2).

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