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Multimedia Tools and Applications

, Volume 77, Issue 17, pp 22319–22338 | Cite as

Hierarchical BoW with segmental sparse coding for large scale image classification and retrieval

  • Jianshe Zhou
  • Narentuya
  • Sheng Tang
  • Jie Liu
Article

Abstract

The bag-of-words (BoW) has been widely regarded as the most successful algorithms for content-based image related tasks, such as large scale image retrieval, classification, and object categorization. Large visual words acquired by BoW quantization through large vocabulary or codebooks have been receiving much attention in the past years. However, not only construction of large vocabulary but also the quantization process impose a heavy burden in terms of time and memory complexities. In order to tackle this issue, we propose an efficient hierarchical BoW (HBoW) to achieve large visual words through quantization by a compact vocabulary instead of large vocabulary. Our vocabulary is very compact since it is only composed of two small dictionaries which is learned through segmental sparse decomposition of local features. To generate the BoW with large size, we first divide the local features into two half parts, and use the two small dictionaries to compute their sparse codes. Then, we map the two indices of the maximum elements of the two sparse codes to a large set of visual words based upon the fact that data with similar properties will share the same base weighted with the largest sparse coefficient. To further make similar patches have higher probability of select the same dictionary base to get similar BoW vectors, we propose a novel collaborative dictionary learning method by imposing the similarity regularization factor together with the row sparsity regularization across data instances during group sparse coding. Additionally, based on index combination of top-2 large sparse codes of local descriptors, we propose a soft BoW assignment method so that our proposed HBoW can tolerate different word selection for similar patches. By employing the inverted file structure built through our HBoW, K-nearest neighbors (KNN) can be efficiently retrieved. After incorporation of our fast KNN search into the SVM-KNN classification method, our HBoW can be used for efficient image classification and logo recognition. Experiments on serval well-known datasets show that our approach is effective for large scale image classification and retrieval.

Keywords

Bag of words Dictionary learning Sparse coding Image retrieval Image classification 

References

  1. 1.
    Avrithis Y, Kalantidis Y (2012) Approximate gaussian mixtures for large scale vocabularies. In: Proc of ECCVGoogle Scholar
  2. 2.
    Chua T-S, Tang S, Trichet R, Tan HK, Song Y (2009) Moviebase: A movie database for event detection and behavioral analysis. In: Proceedings of ACM multimedia 2009 workshop on Web-Scale multimedia corpusGoogle Scholar
  3. 3.
    Deng J, Berg AC, Li K, Fei-Fei L (2010) What does classifying more than 10,000 image categories tell us? . In: Proc of the 11th European conference on computer vision: Part V, ECCV’10. Springer, Berlin, pp 71–84Google Scholar
  4. 4.
    Deng J, Dong W, Socher R, Li L-J, Li K, Fei-fei L (2009) Imagenet: A large-scale hierarchical image database. In: Proc of conference on computer vision and pattern recognition (CVPR). http://image-net.org/
  5. 5.
    Girod B, Chandrasekhar V, Chen DM, Cheung NM, Grzeszczuk R, Reznik Y, Takacs G, Tsai SS, Vedantham R. (2011) Mobile visual search. IEEE Signal Processing Magazine, Special Issue on Media Search in Mobile Devices 28 (4):61–76CrossRefGoogle Scholar
  6. 6.
    Hastie T, Iain J, Efron B, Tibshirani R (2004) Least angle regression. Ann Stat 32(2):407–499MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Jegou H, Douze M, Schmid C (2010) Improving bag-of-features for large scale image search. Int J Comput Vis 87(3):316–336CrossRefGoogle Scholar
  8. 8.
    Jegou H, Douze M, Schmid C (2011) Product quantization for nearest neighbor search. IEEE Trans Pattern Anal Mach Intell 33:117–128CrossRefGoogle Scholar
  9. 9.
    Jiang Y-G, Yang J, Ngo C-W, Hauptmann AG (2010) Representations of keypoint-based semantic concept detection A comprehensive study. IEEE Trans Multimedia 12(1):42–53CrossRefGoogle Scholar
  10. 10.
    Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791CrossRefMATHGoogle Scholar
  11. 11.
    Li D, Yang L, Hua XS, Zhang HJ (2010) Large-scale robust visual codebook construction. In: ACM Multimedia ’10Google Scholar
  12. 12.
    Li P, Lu X, Wang Q (2015) From dictionary of visual words to subspaces Locality-constrained affine subspace coding. In: Proc of conference on computer vision and pattern recognition (CVPR), pp 2348–2357Google Scholar
  13. 13.
    Li Y, Tang S, Lin M, Zhang Y, Li J, Yan S (2018) Implicit negative sub-categorization and sink diversion for object detection. IEEE Trans Image Process 27(4):1561–1574MathSciNetCrossRefGoogle Scholar
  14. 14.
    Liu J, Tang S, Li Y (2017) Collaborative dictionary learning and soft assignment for sparse coding of image features. In: MultiMedia Modeling - 23rd International Conference, MMM 2017, Reykjavik, Iceland, January 4-6, 2017, Proceedings, Part I, pp 443–451Google Scholar
  15. 15.
    Mairal J, Bach F, Ponce J, Sapiro G (2010) Online learning for matrix factorization and sparse coding. J Mach Learn Res 11:19–60MathSciNetMATHGoogle Scholar
  16. 16.
    Mantziou E, Papadopoulos S, Kompatsiaris Y (2013) Scalable training with approximate incremental laplacian eigenmaps and pca. In: Proceedings of the 21st ACM international conference on Multimedia, ACM Multimedia, pp 381–384Google Scholar
  17. 17.
    Mikulik A, Perdoch M, Chum O, Matas J (2013) Learning vocabularies over a fine quantization. Int J Comput Vis 103(1):163–175MathSciNetCrossRefGoogle Scholar
  18. 18.
    Muja M, Lowe DG (2014) Scalable nearest neighbor algorithms for high dimensional data. IEEE Trans Pattern Anal Mach Intell 36(11):2227–2240CrossRefGoogle Scholar
  19. 19.
    Nie L, Yan S, Wang M, Hong R, Chua T-S (2012) Harvesting visual concepts for image search with complex queries. In: Proc of ACM multimedia 2012 conferenceGoogle Scholar
  20. 20.
    Nister D, Stewenius H (2006) Scalable recognition with a vocabulary tree. In: Proc of conference on computer vision and pattern recognition (CVPR), pp 2161–2168Google Scholar
  21. 21.
    Petitcolas FAP (2000) Watermarking schemes evaluation. IEEE Signal Process 17(5):117–128CrossRefGoogle Scholar
  22. 22.
    Philbin J, Chum O, Isard M, Sivic J, Zisserman A (2007) Object retrieval with large vocabularies and fast spatial matching. In: Proc of conference on computer vision and pattern recognition (CVPR), pp 1–8Google Scholar
  23. 23.
    Philbin J , Chum O, Isard M, Sivic J, Zisserman A (2008) Lost in quantization: improving particular object retrieval in large scale image databases. In: Proc of conference on computer vision and pattern recognition (CVPR)Google Scholar
  24. 24.
    Sivic J, Zisserman A (2003) Video google: a text retrieval approach to object matching in videos. In: Proc of ICCV, pp 1470–1477Google Scholar
  25. 25.
    Strelow D, Bengio S, Pereira F, Singer Y (2009) Group sparse coding. In: Neural information processing systems - NIPSGoogle Scholar
  26. 26.
    Stricker MA, Orengo M (1995) Similarity of color images. In: SPIE conference on storage and retrieval for image and video databases III, vol 2420, pp 381–392Google Scholar
  27. 27.
    Tang S, Chen H, Lv K, Zhang YD (2015) Large visual words for large scale image classification. In: 2015 IEEE international conference on image processing (ICIP), pp 1170–1174Google Scholar
  28. 28.
    Tang S, Li J-T, Li M, Xie C, Liu Y-Z, Tao K, Xu S-X (2008) TRECVID 2008 High-Level feature extraction By MCG-ICT-CAS. In: Proceedings of TRECVID 2008 WorkshopGoogle Scholar
  29. 29.
    Tang S, Li Y, Deng L, Zhang Y-D (2017) Object localization based on proposal fusion. IEEE Trans Multimedia 19(9):2105–2116CrossRefGoogle Scholar
  30. 30.
    Tang S, Zhang YD, Chen H (2015) Scalable logo recognition based on compact sparse dictionary for mobile devices. In: 2015 IEEE 17th international workshop on multimedia signal processing (MMSP), pp 1–6Google Scholar
  31. 31.
    Tang S, Zhang YD, Xua Z-X, Li H, Zheng Y-T, Li J-T (2015) An efficient concept detection system via sparse ensemble learning. Neurocomputing 69:124–133CrossRefGoogle Scholar
  32. 32.
    Tang S, Zheng Y-T, Wang Y, Chua T-S (2012) Sparse ensemble learning for concept detection. IEEE Trans Multimedia 14(1):43–54CrossRefGoogle Scholar
  33. 33.
    Wang M, Hua X-S, Hong R, Tang J, Qi G-J, Song Y (2010) Unified video annotation via multi-graph learning. IEEE Trans Circuits Syst Video Technol 19 (5):733–746CrossRefGoogle Scholar
  34. 34.
    Wu C (2007) SiftGPU: a GPU implementation of scale invariant feature transform (SIFT) http://cs.unc.edu/ccwu/siftgpu
  35. 35.
    Xie H, Ke G, Zhang Y, Tang S, Li J, Liu Y (2011) Efficient feature detection and effective post-verification for large scale near-duplicate image search. IEEE Trans Multimedia 13(6):1319–1332CrossRefGoogle Scholar
  36. 36.
    Yang J, Yu K, Gong Y, Huang T (2009) Linear spatial pyramid matching using sparse coding for image classification. In: Proc of Conference on Computer Vision and Pattern Recognition (CVPR)Google Scholar
  37. 37.
    Zhang H, Berg AC, Maire M, Malik J (2006) Svm-knn: Discriminative nearest neighbor classification for visual category recognition. In: Proc of conference on computer vision and pattern recognition (CVPR), pp 2126–2136Google Scholar
  38. 38.
    Zhang YD, Wang Y, Tang S, Hoi SCH, Li JT (2014) Fsph: fitted spectral hashing for efficient similarity search. Comput Vis Image Underst 124:3–11CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Beijing Advanced Innovation Center for Imaging TechnologyCapital Normal UniversityBeijingPeople’s Republic of China
  2. 2.Institute of Computing TechnologyChinese Academy of SciencesBeijingPeople’s Republic of China
  3. 3.College of Information and EngineeringCapital Normal UniversityBeijingPeople’s Republic of China

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