Total views : 506

Active Salient Component Classifier System on Local Features for Image Retrieval

Affiliations

  • Department of Computer Science, Gobi Arts and Science College, Erode – 638452, Tamil Nadu, India
  • Department of Computer Science, Erode Arts and Science College, Erode – 638112, Tamil Nadu, India
  • Department of Computer Science, Kamban College of Arts and Science, Coimbatore, India

Abstract


Objectives: The objective of the examined active salient component classifier is to extract the image features from the visual substance through a novel non-parametric example based positioning methodology to utilizing comparability and learning strategies. Methods/Statistical Analysis: To establish the authenticity to avoid improper retrieval on online based image classification system called, lite Dynamic Pattern Extraction Algorithm (DPEA) for Image Classification and salient image extraction algorithm is designed. DPEA Algorithm on each benchmark dataset works on user query basis and reducing the overhead incurred during user query processing by applying labeling process. Next, by retrieving salient features on images with zero mean for each client query and each image retrieval reduces the execution time and complexity as the database does not maintain the related features. Finally, the Potential image Classification and retrieval prevents the unauthorized user modification on image data, therefore improving the reality. Here a Web Image Dataset (NUS-WIDE) created by lab of media search in National University of Singapore. The computer vision CIFAR-10 dataset, Modified National Institute of Standards and Technology database of USA (MNIST) dataset is used for experiment. A series of simulation results are performed to test the image retrieval efficiency, execution time, performance fitness for obtaining efficiency of image data handling and measure the effectiveness of DPEA Algorithm. Findings: The tests were directed on the dataset for image classification and retrieval on each region of the images. The Proposed DPEA offers better performance with an improvement of the data confidentiality by 18.72%, reduces execution complexity by 7.98%, reduces average retrieval overhead by 6.65% and also minimize retrieval complexity by 15.57% compared to existing algorithms of LFDA, PCA and PCCA respectively. Application/Improvements: It can be further extended with implementation of new retrieval techniques with different parameters which improves more retrieval efficiency and performance integrity.

Keywords

Bit Streams, Component Analysis, Dynamic Patterns, Feature Extraction, Image Retrieval, Salient Features.

Full Text:

 |  (PDF views: 105)

References


  • Abry P, Coddington J, Daubechies I, Hendriks E, Hughes S, Johnson R, Postma E. Special issue on image processing for digital art work guest editor’s forewords. Signal Process. 2013; 93:525–6.Crossref
  • Zhang B, Gao Y, Zhao S, Liu J. Local derivative pattern versus local binary pattern: Face recognition with highorder local pattern descriptor. IEEE Transactions on Image Processing. 2010; 19(2):533–44.Crossref
  • Chechik G, Sharma V, Shalit U, Bengio S. Large scale online learning of image similarity through ranking. Journal of Machine Learning Research. 2010; 11:1109–35.
  • Chum O, Matas J. Large-scale discovery of spatially related images. IEEE Transactions Pattern Analysis Machcine Intelligence. 2010; 32(2):371–7.Crossref
  • Deepika NP, Subha LMS, Gopal V. Pattern extraction in segmented satellite images by reducing semantic gap using relevance feedback mechanism. Procedia Computer Science. 2015; 46:1809–16.Crossref
  • Ding S, Lin L, Wang G, Chao H. Deep feature learningwith relative distance comparison for person re-identification. Pattern Recognition. 2015; 48(10):2993–3003.Crossref
  • Gao S, Tsang IW, Chia L. Laplacian sparse coding, hypergraph laplacian sparse coding, and applications, IEEE Trans. Pattern Anal.Mach. Intell. 2013, 35(1), pp. 92–104.Crossref
  • Go Irie XMW, Li Z, Chang S. Locally linear hashing for extracting non-linear manifolds. CVPR; 2014.
  • Zhi L, Zhang S, Zhao D. Combining similarity measures in content-based image retrieval guided by mutual information. Image and Graphics. 2011; 16(10):1850–7.
  • Lin L, Wang X, Yang W, Lai H. Discriminatively trained and-or graph models for object shape detection. IEEE Transactions on Pattern Analysis and Machine Intelligence.
  • ; 37(5):959–72.Crossref
  • Liu W, Wang J, Ji R, Jiang Y, Chang S. Supervised hashing with kernels. CVPR. 2012.
  • Liu Z, Li H, Zhou W, Zhao R, Tian Q. Contextual hashing for large-scale image search. IEEE Transactions on Image Processing. 2014; 23(4):1606–14.Crossref
  • Madhusudhana Rao TV, Setty PS, Srinivas Y. An efficient system for medical image retrieval using generalized gamma distribution. International Journal of Image, Graphics and Signal Processing. 2015; 6:52–8. Crossref
  • Farooq M. Optimizing pattern recognition scheme using genetic algorithms in computer image processing. International Journal of Advanced Research in Computer Engineering and Technology. 2015; 4(3):834–6.
  • Alam MS, Alam MA. Advances in pattern recognition algorithms, architectures and devices. Optical Engineering. 2004; 43(8):1702–4. Crossref
  • Ojala T, Pietikainen M, Maenpaa T. Multiresolution grayscale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell. 2002 Jul; 24(7):971–87. Crossref
  • Otsu N. A threshold selection method from gray-scale histogram. IEEE Transactions on System, Man and Cybernatics. 1978; 8:62–6.
  • Parasher M, Sharma S, Sharma A. K, Gupta JP. Anatomy on pattern recognition. Indian Journal of Computer Science and Engineering. 2011; 2(3):371–8.
  • Prasad BG, Krishna AN. Statistical texturefeature-based retrieval and performance evaluation of CT brain images. Proceedings of the 3rd International Conference on Electronics Computer Technology; Kanyakumari, India. 2011. p. 289-93.
  • Pylarinos D, Theofilatos K, Siderakis K, Thalassinakis E. Discharges classification using genetic algorithms and feature selection algorithms on time and frequency domain data extracted from leakage current measurements. Engineering, Technology ans Applied Science Research. 2013. 3(6):544–8.
  • Raginsky M, Lazebnik S. Locality-sensitive binary codes from shift-invariant kernels. Advances in Neural Information Processing Systems. 2009; 22:1509–17.
  • Reza AW, Eswaran C, Hati S. Automatic tracing of optic disc and exudates from color fundus images using fixed and variable thresholds, Journal of Medical System. 2008; 33:73–80.Crossref
  • Zhang R, Lin L, Zhang R, Zuo W, Zhang L. Bit-Scalable Deep Hashing with Regularized Similarity Learning for Image Retrieval and Person Re-identification. IEEE Transactions on Image Processing. 2015; 24(12):1-14. Crossref
  • Shao J, Wu F, Ouyang C, Zhang X. Sparse spectral hashing. Pattern Recognition Letters. 2012; 33(3):271–7.Crossref 25. Sharma P, Kaur M. Classification in pattern recognition: A review. International Journal of Advanced Research in Computer Science and Software Engineering. 2013 Apr; 3(4): 298-306.
  • Walker JR, MacKenzie WM, Mecikalski JR, Jewett CP. An enhanced geostationary satellite-based convective initia tion algorithm for 0–2-h now casting with object tracking Journal of Applied Meteorology and Climatology. 2012; 51:1931–49. Crossref
  • Liua W, Yub Z, Lua L, Wenc Y, Lia H, Zoua Y. KCRC-LCD: Discriminative kernel collaborative representation with locality constrained dictionary for visual categorization. Pattern Recognition. 2014;1-16.
  • Wu Z, Ke Q, Sun J, Shum H-Y. A multi-sample, multi-tree approach to bag-of-words image representation for image retrieval. International Conference on Computer Vision; 2009.
  • Zhang B, Shan S, Chen X, Gao W. Histogram of Gabor Phase Patterns (HGPP): A novel object representation approach for face recognition. IEEE Trans Image Processing. 2007 Jan; 16(1):57–68. Crossref
  • Zhu X, Zhang L, Huang Z. A sparse embedding and least variance encoding approach to hashing. IEEE Transactions on Image Processing. 2014; 23(9):3737–50. Crossref

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.