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Artificial Intelligence Review

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27.05.2019

Enhanced bag of visual words representations for content based image retrieval: a comparative study

verfasst von: K. S. Arun, V. K. Govindan, S. D. Madhu Kumar

Erschienen in: Artificial Intelligence Review | Ausgabe 3/2020

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Abstract

The exponential growth of digital image data poses numerous open problems to computer vision researchers. In this regard, designing an efficient and more accurate mechanism that finds and retrieve desired images from large repositories is of greater importance. To this end, various types of content based image retrieval (CBIR) systems have been developed. A typical CBIR system enables the search and retrieval of desired images from large databases that are similar to a given query image by means of automatically extracted visual features from image pixels. In CBIR domain, the bag of visual words (BoVW) model is one of the most widely used feature representation scheme and there exist a number of image retrieval frameworks based on BoVW model. It has been observed that most of them demonstrated promising results for the task of medium and large scale image retrieval. However, image retrieval literature lacks a comparative evaluation of these extended BoVW formulations. To this end, this paper aims to categorize and evaluate the existing BoVW model based formulations for the task of content based image retrieval. The commonly used datasets and the evaluation metrics to assess the retrieval effectiveness of these existing models are discussed. Moreover, quantitative evaluation of state of the art image retrieval systems based on BoVW model is also provided. Finally, certain promising directions for future research are proposed on the basis of the existing models and the demand from real-world.

Vorheriger Artikel Generation of maximal fuzzy cliques of fuzzy permutation graph and applications

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Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 102:2037–2041CrossRef

Arandjelovic R, Gronat P, Torii A, Pajdla T, Sivic J (2016) NetVLAD: CNN architecture for weakly supervised place recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5297–5307

Blei DM, Lafferty JD (2005) Correlated topic models. In: Proceedings of the 18th international conference on neural information processing systems, MIT Press, Cambridge, MA, USA, pp 147–154

Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3(Jan):993–1022MATH

Boulemden A, Tlili Y (2012) Image indexing and retrieval with pachinko allocation model: application on local and global features. In: Proceedings of the 12th pacific rim conference on knowledge management and acquisition for intelligent systems, Springer, Berlin, Heidelberg, pp 140–146

Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, New York, NYCrossRef

Cai J, Zha Z-J, Luan H, Zhang S, Tian Q (2013) Learning attribute-aware dictionary for image classification and search. In: Proceedings of the 3rd ACM international conference on multimedia retrieval, ACM, pp 33–40

Cao Y, Wang C, Li Z, Zhang L, Zhang L (2010) Spatial-bag-of-features. In: Proceedings of the 2010 IEEE conference on computer vision and pattern recognition, IEEE, pp 3352–3359

Chen SS, Donoho DL, Saunders MA (1998) Atomic decomposition by basis pursuit. SIAM J Sci Comput 20(1):33–61MathSciNetCrossRef

Chiang C-C, Wu J-W, Lee GC (2012) Probabilistic semantic component descriptor. Multimed Tools Appl 59(2):629–643CrossRef

Engan K, Aase SO, Husoy JH (1999) Method of optimal directions for frame design. In: Proceedings of the IEEE international conference on acoustics, speech, and signal processing, vol 5, IEEE, pp. 2443–2446

Gao Y, Ji R, Liu W, Dai Q, Hua G (2014) Weakly supervised visual dictionary learning by harnessing image attributes. IEEE Trans Image Process 23(12):5400–5411MathSciNetCrossRef

Ge T, Ke Q, Sun J (2013) Sparse-coded features for image retrieval. In: BMVC

Gehler PV, Holub AD, Welling M (2006) The rate adapting poisson model for information retrieval and object recognition. In: Proceedings of the 23rd international conference on machine learning, ACM, pp 337–344

Greif T, Hörster E, Lienhart R (2008) Correlated topic models for image retrieval. Technical report, University of Augsburg, Germany, July

Grubinger M, Clough P, Müller H, Deselaers T (2006) The IAPR TC-12 benchmark: a new evaluation resource for visual information systems. In: Proceedings of international conference on language resources and evaluation, vol 5, ELRA, p 10

Hinton G (2010) A practical guide to training restricted boltzmann machines. Momentum 9(1):926–947

Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507MathSciNetCrossRef

Hofmann T (2001) Unsupervised learning by probabilistic latent semantic analysis. Mach Learn 42(1):177–196CrossRef

Hörster E, Lienhart R, Effelsberg W, Möller B (2009) Topic models for image retrieval on large-scale databases. ACM Sigmultimed Rec 1(4):15–16CrossRef

Huang Y, Huang K, Yu Y, Tan T (2011) Salient coding for image classification. In: Proceedings of the IEEE international conference on computer vision and pattern recognition, IEEE Computer Society, pp 1753–1760

Huang Y, Wu Z, Wang L, Tan T (2014) Feature coding in image classification: a comprehensive study. IEEE Trans Pattern Anal Mach Intell 36(3):493–506CrossRef

Huiskes MJ, Thomee B, Lew MS (2010) New trends and ideas in visual concept detection: the MIR Flickr retrieval evaluation initiative. In: Proceedings of international conference on multimedia information retrieval (ACM), pp 527–536

Jacobs CE, Finkelstein A, Salesin DH (1995) Fast multiresolution image querying, In: Proceedings of the 22nd annual conference on Computer graphics and interactive techniques, ACM, pp 277–286

Jegou H, Douze M, Schmid C (2008) Hamming embedding and weak geometric consistency for large scale image search. In: Proceedings of the 10th European conference on computer vision: Part I, Springer, Berlin, Heidelberg, pp 304–317

Jégou H, Douze M, Schmid C, Pérez P (2010) Aggregating local descriptors into a compact image representation. In: Proceedings of international conference on computer vision and pattern recognition, IEEE, pp 3304–3311

Karaman S, Benois-Pineau J, Mégret R, Bugeau A (2012) Multi-layer local graph words for object recognition. In: Proceedings of the 18th international conference on advances in multimedia modeling, Springer, Berlin, pp 29–39

Koch G, Zemel R, Salakhutdinov R (2015) Siamese neural networks for one-shot image recognition. In: ICML deep learning workshop, vol 2

Kogler M, Lux M (2010) Bag of visual words revisited: an exploratory study on robust image retrieval exploiting fuzzy codebooks. In: Proceedings of the tenth international workshop on multimedia data mining, ACM, New York, NY, USA, pp 3–136

Koller D, Friedman N (2009) Probabilistic graphical models: principles and techniques- adaptive computation and machine learning. The MIT press, Cambridge

Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of the 2006 IEEE computer society conference on computer vision and pattern recognition, vol 2, IEEE Computer Society, Washington, DC, USA, pp 2169–2178

Le Pennec E, Mallat S (2005) Bandelet image approximation and compression. Multiscale Model Simul 4(3):992–1039MathSciNetCrossRef

Li P, Cheng J, Li Z, Lu H (2011) Correlated PLSA for image clustering. In: Proceedings of the 17th international conference on advances in multimedia modeling, vol Part I, Springer, Berlin, Heidelberg, pp 307–316

Li W, McCallum A (2006) Pachinko allocation: DAG-structured mixture models of topic correlations. In: Proceedings of the 23rd international conference on machine learning, ACM, pp 577–584

Lienhart R, Romberg S, Hörster E (2009) Multilayer pLSA for multimodal image retrieval. In: Proceedings of the ACM international conference on image and video retrieval, ACM, p. 9

Liu G-H, Yang J-Y, Li Z (2015) Content-based image retrieval using computational visual attention model. Pattern Recognit 48(8):2554–2566CrossRef

Liu L, Wang L, Liu X (2011) In defense of soft-assignment coding. In: Proceedings of the 2011 international conference on computer vision, IEEE Computer Society, Washington, DC, USA, pp 2486–2493

Lloyd S (1982) Least squares quantization in PCM. IEEE Trans Inf Theory 28(2):129–137MathSciNetCrossRef

Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110CrossRef

Mairal J, Bach F, Ponce J, Sapiro G (2009) Online dictionary learning for sparse coding. In: Proceedings of the 26th annual international conference on machine learning, ACM, pp 689–696

Nowak E, Jurie F, Triggs B (2006) Sampling strategies for bag-of-features image classification. In: Proceedings of the 9th European conference on computer vision, vol Part IV, Springer, Berlin, pp 490–503

Passalis N, Tefas A (2017) Learning bag-of-features pooling for deep convolutional neural networks. In: Proceedings of the IEEE international conference on computer vision, pp 5755–5763

Pedrosa GV, Traina AJ (2013) From bag-of-visual-words to bag-of-visual-phrases using n-grams. In: Proceedings of the 26th conference on graphics, patterns and images, IEEE, pp 304–311

Penatti OA, Silva FB, Valle E, Gouet-Brunet V, Torres RDS (2014) Visual word spatial arrangement for image retrieval and classification. Pattern Recognit 47(2):705–720CrossRef

Perronnin F, Sánchez J, Mensink T (2010) Improving the Fisher Kernel for large-scale image classification. In: Proceedings of the 11th European conference on computer vision: Part IV, Springer, Berlin, Heidelberg, pp 143–156

Philbin J, Chum O, Isard M, Sivic J, Zisserman A (2007) Object retrieval with large vocabularies and fast spatial matching. In: Proceedings of the IEEE conference on computer vision and pattern recognition, IEEE, pp 1–8

Quelhas P, Monay F, Odobez J-M, Gatica-Perez D, Tuytelaars T (2007) A thousand words in a scene. IEEE Trans Pattern Anal Mach Intell 29(9):1575–1589CrossRef

Rahman MM, Bhattacharya P, Desai BC (2009) A unified image retrieval framework on local visual and semantic concept-based feature spaces. J Vis Commun Image Represent 20(7):450–462CrossRef

Salakhutdinov R, Hinton G (2009) Replicated softmax: an undirected topic model. In: Proceedings of the 22nd international conference on neural information processing systems, Curran Associates Inc., USA, pp 1607–1614

Saxe AM, Mcclelland JL, Ganguli S (2014) Exact solutions to the nonlinear dynamics of learning in deep linear neural network. In: International conference on learning representations

Skretting K, Engan K (2010) Recursive least squares dictionary learning algorithm. IEEE Trans Signal Process 58(4):2121–2130MathSciNetCrossRef

Srinivas M, Naidu RR, Sastry CS, Mohan CK (2015) Content based medical image retrieval using dictionary learning. Neurocomputing 168:880–895CrossRef

Tariyal S, Majumdar A, Singh R, Vatsa M (2016) Greedy deep dictionary learning. arXiv preprint arXiv:1602.00203

Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B Methodol 58:267–288MathSciNetMATH

Tishby N, Pereira FC, Bialek W (2000) The information bottleneck method. arXiv preprint arXiv:physics/0004057

Tropp JA, Gilbert AC (2007) Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans Inf Theory 53(12):4655–4666MathSciNetCrossRef

Vedaldi A, Fulkerson B (2010) VLFeat: an open and portable library of computer vision algorithms. In: Proceedings of the 18th ACM international conference on multimedia, ACM, pp 1469–1472

Wang J, Yang J, Yu K, Lv F, Huang T, Gong Y (2010) Locality-constrained linear coding for image classification. In: Proceedings of international conference on computer vision and pattern recognition, IEEE, pp 3360–3367

Wu Z, Huang Y, Wang L, Tan T (2012) Group encoding of local features in image classification. In: Proceedings of the 21st international conference on pattern recognition, IEEE, pp 1505–1508

Yang M, Zhang L, Feng X, Zhang D (2011) Fisher discrimination dictionary learning for sparse representation. In: Proceedings of the 2011 international conference on computer vision, IEEE Computer Society, Washington, DC, USA, pp 543–550

Yu K, Zhang T, Gong Y (2009) Nonlinear learning using local coordinate coding. In: Proceedings of advances in neural information processing systems, pp 2223–2231

Zhang D, Lu G (2003) Evaluation of similarity measurement for image retrieval. In: Proceedings of the 2003 international conference on neural networks and signal processing, 2003, vol 2, IEEE, pp 928–931

Zhou N, Fan J (2014) Jointly learning visually correlated dictionaries for large-scale visual recognition applications. IEEE Trans Pattern Anal Mach Intell 36(4):715–730MathSciNetCrossRef

Zhou W, Kamata S-i (2012) Face recognition with learned local curvelet patterns and 2-directional l1-norm based 2DPCA. In: Asian conference on computer vision, Springer, pp 109–120

Titel: Enhanced bag of visual words representations for content based image retrieval: a comparative study
verfasst von: K. S. Arun
V. K. Govindan
S. D. Madhu Kumar
Publikationsdatum: 27.05.2019
Verlag: Springer Netherlands
Erschienen in: Artificial Intelligence Review / Ausgabe 3/2020
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-019-09715-6

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