Multimedia Tools and Applications

, Volume 77, Issue 3, pp 4081–4092 | Cite as

Scene image classification using locality-constrained linear coding based on histogram intersection



Recently linear Spatial Pyramid Matching (SPM) method based on sparse coding has achieved great success in image classification. The raise of Locality-constrained Linear Coding (LLC) proves the importance of locality. In this paper, we propose an improved feature coding scheme called Locality-constrained Linear Coding Based on Histogram Intersection (HILLC). HILLC uses histogram intersection to describe the distance between feature vector and codebook. For each feature vector, search the KNN nearest neighbors to construct a local codebook. Compared with LLC, HILLC can obtain more robust codes. Experimental results demonstrate that our proposed method outperforms other related coding methods.


SPM Sparse coding Histogram intersection Image classification 



This project is partly supported by NSF of China (61202134, 31671006), the Natural Science Foundation of Jiangsu Province (No.BK20140638, BK2012437).


  1. 1.
    Aharon M, Elad M (2006) Bruckstein The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 54(11):4311–4322CrossRefMATHGoogle Scholar
  2. 2.
    Ahonen T, Hadid A, Pietikainen M (2004) Face recognition with local binary patterns. ECCV, pp:469–481Google Scholar
  3. 3.
    Barla A, Odone F, Verri A (2003) Histogram intersection kernel for image classification. International Conference on Image Processing, 3(2): III-513-16.Google Scholar
  4. 4.
    Bin G, Victor S (2015) Sheng, Zhijie Wang, Derek Ho, Said Osman, and Shuo Li. Incremental learning for v-Support Vector Regression Neural Networks 67:140–150. doi: 10.1016/j.neunet.2015.03.013 Google Scholar
  5. 5.
    Bin G, Victor S, Sheng KYT, Romano W, Li S (2015) Incremental support vector learning for ordinal regression. IEEE Transactions on Neural Networks and Learning Systems 26(7):1403–1416. doi: 10.1109/TNNLS.20142342533 MathSciNetCrossRefGoogle Scholar
  6. 6.
    Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. Proceedings of the fifth annual workshop on computational learning theory, pp. 144-152Google Scholar
  7. 7.
    Chang CC, Lin CJ (2001) LIBSVM: a library for support vector machines [EB/OL]
  8. 8.
    Chen Y (2016) Wavelet energy entropy and linear regression classifier for detecting abnormal breasts. Multimedia Tools and Applications. doi: 10.1007/s11042-016-4161-0 (Online)
  9. 9.
    Chen Y, Ma J, Feng Q, Luo L, Shi P, Chen W (2008) Nonlocal prior Bayesian tomographic reconstruction. J Math Imaging and Vision 30(2):133–146CrossRefGoogle Scholar
  10. 10.
    Chen Y, Shi L, Feng Q, Yang J, Shu H, Luo L, Coatrieux J-L, Chen W (2014) Artifact suppressed dictionary learning for low-dose ct image processing. IEEE, Trans Med Imaging 33(12):2271–2292CrossRefGoogle Scholar
  11. 11.
    Chen Y, Zhang Y, Yang J, Cao Q, Yang G, Chen J, Shu H, Luo L, Coatrieux J, Feng Q (2016) Curve-like structure extraction using minimal path propagation with backtracking. IEEE, Trans Image Process 25(2):988–1003MathSciNetCrossRefGoogle Scholar
  12. 12.
    Chen MM, Li Y et al (2016) Morphological analysis of dendrites and spines by hybridization of ridge detection with twin support vector machine. Peer J 4:e2207CrossRefGoogle Scholar
  13. 13.
    Dong Z et al (2015) Magnetic Resonance brain image classification via stationary wavelet Transform and generalized eigenvalue proximal support vector machine. J Med Imaging Health Informatics 5(7):1395–1403CrossRefGoogle Scholar
  14. 14.
    Fei-Fei L, Perona P (2005) A bayesian hierarchical model for learning natural scene categories. IEEE Computer Vision and Pattern Recognition. New York: IEEE, 524–531Google Scholar
  15. 15.
    Gu B (2015) Et. Al, Incremental learning for ν-support vector regression. Neural Netw 67:140–150CrossRefGoogle Scholar
  16. 16.
    Gu B, Sheng VS (2016) A robust regularization path algorithm for ν-support vector classification, IEEE Transactions on Neural Networks and Learning Systems, doi:  10.1109/TNNLS.2016.2527796
  17. 17.
    Hartigan JA, Wong MA (1979) Algorithm AS 136: a k-means clustering algorithm. Appl Stat 28(1):100–108CrossRefMATHGoogle Scholar
  18. 18.
    Hornik K (1991) Approximation capabilities of feedforward networks. Neural Netw:251–257Google Scholar
  19. 19.
    Huang SM, Yang JF (2012) Kernel linear regression for low resolution face recognition under variable illumination, in: IEEE International Conf. on Acoustics, Speech and Signal Processing(ICASP), PP. 1945–1948Google Scholar
  20. 20.
    Huang SM, Yang JF (2012) Improved principal component regression for face recognition under illumination variations. IEEE Signal Process. Lett. 19(4):179–182CrossRefGoogle Scholar
  21. 21.
    Huang SM, Yang JF (2013) Linear discriminant regression classification for face recognition. IEEE Signal Process Lett 20(1):91–94CrossRefGoogle Scholar
  22. 22.
    Huang SM, Yang JF (2013) Unitary regression classification with total minimum projection error for face recognition. IEEE Signal Process. Lett. 20(5):443–446MathSciNetCrossRefGoogle Scholar
  23. 23.
    Huang GB, Zhu QY, Slew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501CrossRefGoogle Scholar
  24. 24.
    Huang GB, Ding XJ, Zhou HM (2010) Optimization method based on extreme learning machine for classification. Neurocomputing 74:155–163CrossRefGoogle Scholar
  25. 25.
    Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. IEEE Computer Society Conference on Computer Vision and Pattern Recognition New York, 2169–2178Google Scholar
  26. 26.
    Lee H, Battle A, Raina R et al (2006) Efficient sparse coding algorithms. Adv Neural Inf Proces Syst:801–808Google Scholar
  27. 27.
    Leshno M, Lin VY, Pinkus A, Schocken S (1993) Multilayer feedforward networks with a nonpolynomial activation function can approximate any function. Neural Netw (6):861–867Google Scholar
  28. 28.
    Li LJ, Fei- Fei L (2007) What, where and who? Classifying events by scene and object recognition, Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on. IEEE, 1–8Google Scholar
  29. 29.
    Li C-H, Kuo B-C, Lin C-T, Huang C-S (2012) A spatial-contextual support vector machine for remotely sensed image classification. IEEE Trans. Geosci. Remote Sens 50(3):784–799CrossRefGoogle Scholar
  30. 30.
    Li Y, Shao Y, Cattani C (2017) Detection of dendritic spines using wavelet packet entropy and fuzzy support vector machine. CNS Neurol Disord Drug Targets 16(2):116–121CrossRefGoogle Scholar
  31. 31.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110CrossRefGoogle Scholar
  32. 32.
    Lu S Y, Yang J Q (2016) Dual-tree complex wavelet transform and twin support vector machine for pathological brain detection, Appl Sci, 6(6), Article ID: 169Google Scholar
  33. 33.
    Moustakidis S, Mallinis G, Koutsias N, Theocharis JB, Petridis V (2012) SVM-based fuzzy decision trees for classification of high spatial resolution remote sensing images. IEEE Trans. Geosci, Remote Sens 50(1):149–169CrossRefGoogle Scholar
  34. 34.
    Naseem I, Togneri R, Bennamoun M (2010) Linear regression for face recognition. IEEE Trans Pattern Anal Mach Intell 32(11):2106–2112CrossRefGoogle Scholar
  35. 35.
    Oliva A, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vis 42(3):145–175CrossRefMATHGoogle Scholar
  36. 36.
    Samaria F, Harter A (1994) Parameterisation of a Stochastic Model for Human Face Identification. Proceedings of 2nd IEEE Workshop on Applications of Computer Vision, Sarasota FL, December 1994Google Scholar
  37. 37.
    Shen F, Tang Z, Jingsong X (2013) Locality constrained representation based classification with spatial pyramid patches. Nenurocomputing 101:104–115CrossRefGoogle Scholar
  38. 38.
    Vapnik VN (1995) The nature of statistical learning theory. Springer, New YorkGoogle Scholar
  39. 39.
    Wang S (2014) Classification of Alzheimer disease based on structural Magnetic Resonance Imaging by kernel support vector machine decision tree. Prog Electromagn Res - Pier 144:185–191CrossRefGoogle Scholar
  40. 40.
    Wang Q (2016) J, Lin, Y. Yuan Salient band selection for hyperspectral image classification via manifold ranking IEEE Trans Neural Netw Learn Syst 27(6):1279–1289MathSciNetGoogle Scholar
  41. 41.
    Wang SH, Du SD (2017) Alzheimer’s disease detection by pseudo Zernike moment and linear regression classification. CNS Neurol Disord Drug Targets 16(1):11–15MathSciNetCrossRefGoogle Scholar
  42. 42.
    Wang S, Yang XJ (2015) Identification of green, oolong and black teas in China via wavelet packet entropy and fuzzy support vector machine. Entropy 17(10):6663–6682MathSciNetCrossRefGoogle Scholar
  43. 43.
    Wang J, Yang J, Yu K et al (2010) Locality-constrained linear coding for image classification. Computer Vision and Pattern Recognition (CVPR), 2010 I.E. Conference on. IEEE: 3360–3367Google Scholar
  44. 44.
    Wen X (2015) Et. Al, A rapid learning algorithm for vehicle classification. Inf Sci 295(1):395–406Google Scholar
  45. 45.
    Yang J, Yu K, Gong Y, et al (2009) Linear spatial pyramid matching using sparse coding for image classification. Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on IEEE: 1794–1801Google Scholar
  46. 46.
    Yang W, Wang Z, Sun C (2015) A collaborative representation based projections method for feature extraction. Pattern Recogn 48(1):20–27CrossRefGoogle Scholar
  47. 47.
    Yang W, Wang Z, Zhang B (2016) Face recognition using adaptive local ternary patterns method. Neurocomputing 213:183–190CrossRefGoogle Scholar
  48. 48.
    Yang W, Sun C, Zheng W (2016) A regularized Least Square based discriminative projections for feature extraction, Neurocomputing, 175: 198-205(2016.1)Google Scholar
  49. 49.
    Yang ZJ, Lu HM et al (2016) Facial emotion recognition based on biorthogonal wavelet entropy, fuzzy support vector machine, and stratified cross validation. IEEE Access 4:8375–8385CrossRefGoogle Scholar
  50. 50.
    Yang W, Sun C, Zheng W, Ricanek K (2017) Gender classification using 3D statistical models. Multimedia Tools and Appl 76(3):4491–4503Google Scholar
  51. 51.
    Yu K, Zhang T, Gong Y (2009) Efficient sparse coding algorithms. Adv Neural Inf Proces Syst:2223–2231Google Scholar
  52. 52.
    Yu K, Zhang T, Gong Y (2009) Nonlinear learning using local coordinate coding. Adv Neural Inf Proces Syst:2223–2231Google Scholar
  53. 53.
    Zhang L, Zhang D (2016) Evolutionary cost-sensitive extreme learning machine. IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2016.2607757
  54. 54.
    Zhang Y et al (2015) Preclinical diagnosis of Magnetic Resonance (MR) brain images via discrete wavelet packet Transform with Tsallis entropy and generalized eigenvalue proximal support vector machine (GEPSVM). Entropy 17(4):1795–1813CrossRefGoogle Scholar
  55. 55.
    Zhang YD et al (2015) Pathological brain detection in MRI scanning by wavelet packet Tsallis entropy and fuzzy support vector machine, SpringerPlus, 4, Article ID: 716Google Scholar
  56. 56.
    Zhang YD, Zhang Y et al (2017) Seven-layer deep neural network based on sparse autoencoder for voxelwise detection of cerebral microbleed. Multimedia Tools and Applications. doi: 10.1007/s11042-017-4554-8 (Online)
  57. 57.
    Zhou XX, Yang M (2016) Comparison of machine learning methods for stationary wavelet entropy-based multiple sclerosis detection: decision tree, k-nearest neighbors, and support vector machine. Simulation 92(9):861–871CrossRefGoogle Scholar
  58. 57.
    Zhou Z, Wang Y, Jonathan Wu Qm, Yang C, Sun X (2017) Effective and efficient global context verification for image copy detection. IEEE Trans Inf Forensies and Secur 12(1):48–63Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjingChina
  2. 2.School of AutomationSoutheast UniversityNanjingChina

Personalised recommendations