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Fusion of local and global features for effective image extraction

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Abstract

Image extraction methods rely on locating interest points and describing feature vectors for these key points. These interest points provide different levels of invariance to the descriptors. The image signature can be described well by the pixel regions that surround the interest points at the local and global levels. This contribution presents a feature descriptor that combines the benefits of local interest point detection with the feature extraction strengths of a fine-tuned sliding window in combination with texture pattern analysis. This process is accomplished with an improved Moravec method using the covariance matrix of the local directional derivatives. These directional derivatives are compared with a scoring factor to identify which features are corners, edges or noise. Located interest point candidates are fetched for the sliding window algorithm to extract robust features. These locally-pointed global features are combined with monotonic invariant uniform local binary patterns that are extracted a priory as part of the proposed method. Extensive experiments and comparisons are conducted on the benchmark ImageNet, Caltech-101, Caltech-256 and Corel-100 datasets and compared with sophisticated methods and state-of-the-art descriptors. The proposed method outperforms the other methods with most of the descriptors and many image categories.

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References

  1. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE computer society conference on computer vision and pattern recognition, 2005. CVPR 2005, vol 1. IEEE, pp 886–893

  2. Lowe DG (1999) Object recognition from local scale-invariant features. In: The proceedings of the seventh IEEE international conference on computer vision, 1999, vol 2. IEEE, pp 1150– 1157

  3. Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (SURF). Comput Vis Image Underst 110(3):346–359

  4. Liu G-H, Yang J-Y (2013) Content-based image retrieval using color difference histogram. Pattern Recogn 46(1):188–198

    Article  Google Scholar 

  5. Chaudhary MD, Upadhyay AB (2014) Integrating shape and edge histogram descriptor with stationary wavelet transform for effective content based image retrieval. In: International conference on circuit, power and computing technologies (ICCPCT), 2014. IEEE, pp 1522–1527

  6. Agrawal D, Jalal AS, Tripathi R (2013) Trademark image retrieval by integrating shape with texture feature. In: International conference on information systems and computer networks (ISCON), 2013. IEEE, pp 30–33

  7. Harris C, Stephens M (1988) A combined corner and edge detector. In: Alvey vision conference, vol 15, p 50

  8. Wang H, Brady M (1995) Real-time corner detection algorithm for motion estimation. Image Vis Comput 13(9):695–703

    Article  Google Scholar 

  9. Khotanzad A, Hong YH (1990) Invariant image recognition by Zernike moments. IEEE Trans Pattern Anal Mach Intell 12(5):489–497

    Article  Google Scholar 

  10. Rosten E, Drummond T (2006) Machine learning for high-speed corner detection. In: Computer vision–ECCV 2006. Springer, Berlin, pp 430–443

  11. Tuytelaars T, Van Gool L (2004) Matching widely separated views based on affine invariant regions. Int J Comput Vis 59(1): 61–85

  12. Sural S, Qian G, Pramanik S (2002) Segmentation and histogram generation using the HSV color space for image retrieval. In: International conference on image processing, 2002. Proceedings. 2002, vol 2. IEEE, pp II–589

  13. Mikolajczyk K, Schmid C (2005) A performance evaluation of local descriptors. IEEE Trans Pattern Anal Mach Intell 27(10):1615–1630

    Article  Google Scholar 

  14. Gupta E, Kushwah RS (2015) Combination of global and local features using DWT with SVM for CBIR. In: 4th international conference on reliability, infocom technologies and optimization (ICRITO)(trends and future directions), 2015. IEEE, pp 1–6

  15. Li J, Wang JZ (2003) Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Trans Pattern Anal Mach Intell 25(9):1075–1088

    Article  Google Scholar 

  16. Dubey SR, Singh SK, Singh RK (2016) Multichannel decoded local binary patterns for content-based image retrieval. IEEE Trans Image Process 25(9):4018–4032

    Article  MathSciNet  Google Scholar 

  17. Xiao Y, Wu J, Yuan J (2014) mCENTRIST: a multi-channel feature generation mechanism for scene categorization. IEEE Trans Image Process 23(2):823–836

    Article  MathSciNet  Google Scholar 

  18. Zhou Y, Zeng F-Z, Zhao H-M, Murray P, Ren J (2016) Hierarchical visual perception and two-dimensional compressive sensing for effective content-based color image retrieval. Cogn Comput 8(5):877–889

    Article  Google Scholar 

  19. Shrivastava N, Tyagi V (2015) An efficient technique for retrieval of color images in large databases. Comput Electr Eng 46:314–327

    Article  Google Scholar 

  20. Kundu MK, Chowdhury M, Bulò SR (2015) A graph-based relevance feedback mechanism in content-based image retrieval. Knowl-Based Syst 73:254–264

    Article  Google Scholar 

  21. Zeng S, Huang R, Wang H, Kang Z (2016) Image retrieval using spatiograms of colors quantized by Gaussian mixture models. Neurocomputing 171:673–684

    Article  Google Scholar 

  22. Walia E, Pal A (2014) Fusion framework for effective color image retrieval. J Vis Commun Image Represent 25(6):1335–1348

    Article  Google Scholar 

  23. Ashraf R, Bashir K, Irtaza A, Mahmood MT (2015) Content based image retrieval using embedded neural networks with bandletized regions. Entropy 17(6):3552–3580

    Article  Google Scholar 

  24. ElAlami ME (2014) A new matching strategy for content based image retrieval system. Appl Soft Comput 14:407–418

    Article  Google Scholar 

  25. Iqbal K, Odetayo MO, James A (2012) Content-based image retrieval approach for biometric security using colour, texture and shape features controlled by fuzzy heuristics. J Comput Syst Sci 78(4):1258–1277

    Article  MathSciNet  Google Scholar 

  26. Neelima N, Reddy ES (2015) An improved image retrieval system using optimized FCM & multiple shape, texture features. In: 2015 IEEE international conference on computational intelligence and computing research (ICCIC). IEEE, pp 1–7

  27. Youssef SM (2012) ICTEDCT-CBIR: integrating curvelet transform with enhanced dominant colors extraction and texture analysis for efficient content-based image retrieval. Comput Electr Eng 38(5):1358–1376

    Article  Google Scholar 

  28. Lande MV, Bhanodiya P, Jain P (2014) An effective content-based image retrieval using color, texture and shape feature. In: Intelligent computing, networking, and informatics. Springer, India, pp 1163–1170

  29. Xia Y, Wan S, Yue L (2014) A new texture direction feature descriptor and its application in content-based image retrieval. In: Proceedings of the 3rd international conference on multimedia technology (ICMT 2013). Springer, Berlin, pp 143–151

  30. Agarwal S, Verma AK, Singh P (2013) Content based image retrieval using discrete wavelet transform and edge histogram descriptor. In: International conference on information systems and computer networks (ISCON), 2013. IEEE, pp 19–23

  31. Jadhav P, Phalnikar R (2015) SIFT based efficient content based image retrieval system using neural network. Artificial Intelligent Systems and Machine Learning 7(8):234–238

    Google Scholar 

  32. Awad D, Courboulay V, Revel A (2012) Saliency filtering of sift detectors: application to cbir. In: Advanced concepts for intelligent vision systems. Springer, Berlin, pp 290–300

  33. Saad MH, Saleh HI, Konber H, Ashour M (2013) CBIR system based on integration between surf and global features

  34. Velmurugan K, Baboo SS (2011) Content-based image retrieval using SURF and colour moments. Global J Comp Sci Technol 10:11

    Google Scholar 

  35. Barbu T (2014) Pedestrian detection and tracking using temporal differencing and HOG features. Comput Electr Eng 40(4):1072–1079

    Article  Google Scholar 

  36. Albiol A, Monzo D, Martin A, Sastre J, Albiol A (2008) Face recognition using HOG–EBGM. Pattern Recogn Lett 29(10):1537–1543

    Article  Google Scholar 

  37. Felzenszwalb PF, Girshick RB, McAllester D, Ramanan D (2010) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627–1645

    Article  Google Scholar 

  38. Pan S, Sun S, Yang L, Duan F, Guan A (2015) Content retrieval algorithm based on improved HOG. In: 3Rd international conference on applied computing and information technology/2nd international conference on computational science and intelligence (ACIT-CSI), 2015. IEEE, pp 438–441

  39. Murala S, Maheshwari RP, Balasubramanian R (2012) Local tetra patterns: a new feature descriptor for content-based image retrieval. IEEE Trans Image Process 21(5):2874–2886

    Article  MathSciNet  Google Scholar 

  40. Moravec HP (1979) Visual mapping by a robot rover. In: Proceedings of the 6th international joint conference on artificial intelligence, vol 1. Morgan Kaufmann Publishers Inc, pp 598–600

  41. Förstner W, Gülch E (1987) A fast operator for detection and precise location of distinct points, corners and centres of circular features. In: Proceedings of the ISPRS intercommission conference on fast processing of photogrammetric data, pp 281–305

  42. Ojala T, Pietikäinen M, Mäenpää T (2000) Gray scale and rotation invariant texture classification with local binary patterns. In: Computer vision-ECCV 2000. Springer, Berlin, pp 404–420

  43. Ojala T, Valkealahti K, Oja E, Pietikäinen M (2001) Texture discrimination with multidimensional distributions of signed gray-level differences. Pattern Recogn 34(3):727–739

    Article  MATH  Google Scholar 

  44. Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recogn 29(1):51–59

    Article  Google Scholar 

  45. Pietikäinen M, Ojala T, Xu Z (2000) Rotation-invariant texture classification using feature distributions. Pattern Recogn 33:43–52

    Article  Google Scholar 

  46. Stejić Z, Takama Y, Hirota K (2003) Genetic algorithm-based relevance feedback for image retrieval using local similarity patterns. Inf Process Manag 39(1):1–23

    Article  MATH  Google Scholar 

  47. Oertel C, Colder B, Colombe J, High J, Ingram M, Sallee P (2008) Current challenges in automating visual perception. In: Proceedings of IEEE advanced imagery pattern recognition workshop

  48. Stanford vision lab, http://image-net.org/ last accessed on October 2016

  49. Griffin G, Holub A, Perona P (2007) Caltech-256 object category dataset

  50. Fei-Fei L, Fergus R, Perona P (2004) Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. IEEE. CVPR 2004 Workshop on Generative-Model Based Vision

  51. Lai C-C, Chen Y-C (2011) A user-oriented image retrieval system based on interactive genetic algorithm. IEEE Trans Instrum Meas 60:3318–3325

    Article  Google Scholar 

  52. Ali N, Bajwa KB, Sablatnig R, Mehmood Z (2016) Image retrieval by addition of spatial information based on histograms of triangular regions. Comput Electr Eng 54:539–550

    Article  Google Scholar 

  53. Walia E, Pal A (2014) Fusion framework for effective color image retrieval. J Vis Commun Image Represent 25(6):1335–1348

    Article  Google Scholar 

  54. Dubey SR, Singh SK, Singh RK (2015) A multi-channel based illumination compensation mechanism for brightness invariant image retrieval. Multimedia Tools and Applications 74(24):11223–11253

    Article  Google Scholar 

  55. Thepade S, Das R, Ghosh S (2015) Novel technique in block truncation coding based feature extraction for content based image identification. In: Transactions on computational science XXV. Springer, Berlin, pp 55–76

  56. Dalal N, Triggs B (2006) Object detection using histograms of oriented gradients. In: Pascal VOC workshop, ECCV

  57. Hu R, Collomosse J (2013) A performance evaluation of gradient field hog descriptor for sketch based image retrieval. Comput Vis Image Underst 117(7):790–806

    Article  Google Scholar 

  58. Wangming X, Jin W, Xinhai L, Lei Z, Gang S (2008) Application of image SIFT features to the context of CBIR. In: International conference on computer science and software engineering, 2008, vol 4. IEEE, pp 552–555

  59. Xu P, Zhang L, Yang K, Yao H (2013) Nested-SIFT for efficient image matching and retrieval. IEEE MultiMedia 20(3):34–46

    Article  Google Scholar 

  60. Kim S, Yoon K-J, Kweon IS (2008) Object recognition using a generalized robust invariant feature and Gestalt’s law of proximity and similarity. Pattern Recogn 41(2):726–741

    Article  MATH  Google Scholar 

  61. Lee Y-H, Kim Y (2015) Efficient image retrieval using advanced SURF and DCD on mobile platform. Multimedia Tools and Applications 74(7):2289–2299

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of IT, University of Central Punjab, Lahore, Pakistan

    Khawaja Tehseen Ahmed & Muhammad Amjad Iqbal

  2. Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan

    Aun Irtaza

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  1. Khawaja Tehseen Ahmed
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  2. Aun Irtaza
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  3. Muhammad Amjad Iqbal
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Correspondence to Khawaja Tehseen Ahmed.

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Ahmed, K.T., Irtaza, A. & Iqbal, M.A. Fusion of local and global features for effective image extraction. Appl Intell 47, 526–543 (2017). https://doi.org/10.1007/s10489-017-0916-1

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  • DOI: https://doi.org/10.1007/s10489-017-0916-1

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