Abstract
A new image feature descriptor for content-based image retrieval is proposed, named as Square Texton Histogram (STH). STH is derived based on the correlation between texture orientation and color information. Based on julesz’s texton theory ‘Square Texton’ templates are proposed for Image texture analysis. Texture Orientation is computed by using proposed multi texture orientation detector that incorporates horizontal, vertical and diagonal edges information. Features are extracted by correlating texture color and edge orientation by using 4-directional co-occurrence matrix while; the final set of features is obtained by histogram. To find similarity between query and target image, a weighted square-chord distance measure is proposed. The Proposed distance metric integrates the advantages of both bin-by-bin and weighted distance metrics. The proposed STH method is tested on standard dataset’s that are extensively used in CBIR domain, such as Coral5K and Coral10K. STH has good discrimination power of primary visual features.
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References
Agarwal S, Verma A, Singh P (2013) Content based image retrieval using discrete wavelet transform and edge histogram descriptor. In: 2103 I.E. International Conference on Information Systems and Computer Networks (ISCON). IEEE, Mathura, pp 19–23
Ahmad J, Sajjad M, Mehmood I, Baik SW (2015) SSH: Salient structures histogram for content based image retrieval. In: 2015 I.E. International Conference on Network-Based Information Systems (NBiS). IEEE, Taipei, pp 212–217
Ahmad J, Sajjad M, Rho S, Baik SW (2016) Multi-scale local structure patterns histogram for describing visual contents in social image retrieval systems. Multimedia Tools and Applications 75(20):12669–12692
Alkhawlani M, Elmogy M, Elbakry H (2015) Content-based image retrieval using local features descriptors and bag-of-visual words. Int J Adv Comput Sci Appl 6(9):212–219
Alzu’bi A, Amira A, Ramzan N (2015) Semantic content-based image retrieval: A comprehensive study. J Vis Commun Image Represent 32:20–54
Alzu'bi A, Amira A, Ramzan N (2017) Content-based image retrieval with compact deep convolutional features. Neurocomputing 249:95–105
Amores J, Sebe N, Radeva P (2007) Context-based object-class recognition and retrieval by generalized correlograms. IEEE Trans Pattern Anal Mach Intell 29(10)
Babenko A (2015) Lempitsky V Aggregating local deep features for image retrieval. Proceedings of the IEEE international conference on computer vision, In, pp 1269–1277
Babenko A, Slesarev A, Chigorin A, Lempitsky V (2014) Neural codes for image retrieval. In: 2014 European conference on computer vision (ECCV). Springer, Heidelberg, pp 584–599
Banerjee P, Bhunia AK, Bhattacharyya A, Roy PP, Murala S (2017) Local neighborhood intensity pattern: a new texture feature descriptor for image retrieval. arXiv preprint arXiv:1709.02463
Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (SURF). Comput Vis Image Underst 110(3):346–359
Carson C, Belongie S, Greenspan H, Malik J (2002) Blobworld: Image segmentation using expectation-maximization and its application to image querying. IEEE Trans Pattern Anal Mach Intell 24(8):1026–1038
Chen Y, Wang JZ, Krovetz R (2003) An unsupervised learning approach to content-based image retrieval. In: 2003 International Symposium on Signal Processing and Its Applications, Vol.1. IEEE, Paris, pp 197–200
Cross GR, Jain AK (1983) Markov random field texture models. IEEE Trans Pattern Anal Mach Intell 1:25–39
Datta R, Joshi D, Li J, Wang JZ (2008) Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys (Csur) 40(2):5
Dharani T, Aroquiaraj IL (2013) A survey on content based image retrieval. In: 2013 I.E. International Conference on Pattern Recognition, Informatics and Mobile Engineering (PRIME). IEEE, Salem, pp 485–490
Diplaros A, Gevers T, Patras I (2006) Combining color and shape information for illumination-viewpoint invariant object recognition. IEEE Trans Image Process 15(1):1–11
Gebejes A, Huertas R (2013) Texture characterization based on grey-level co-occurrence matrix. In: 2013 Conference of Informatics and Management Sciences, pp 375–378
Gong Y, Wang L, Guo R, Lazebnik S (2014) Multi-scale orderless pooling of deep convolutional activation features. In: 2014 European conference on computer vision. Springer, Heidelberg, pp 392–407
Gordo A, Almazán J, Revaud J, Larlus D (2016) Deep image retrieval: Learning global representations for image search. In: 2016 European Conference on Computer Vision. Springer, Heidelberg, pp 241–257
Guo Z, Zhang L, Zhang D (2010) Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern Recogn 43(3):706–719
Han J, Ma K-K (2007) Rotation-invariant and scale-invariant Gabor features for texture image retrieval. Image Vis Comput 25(9):1474–1481
Huang J, Kumar SR, Mitra M, Zhu W-J, Zabih R (1997) Image indexing using color correlograms. In: 1997 I.E. Conference on Computer Vision and Pattern Recognition. IEEE, San Juan, pp 762–768
Huang Z-C, Chan PP, Ng WW, Yeung DS (2010) Content-based image retrieval using color moment and gabor texture feature. In: 2010 International Conference on Machine Learning and Cybernetics (ICMLC), Vol. 2. IEEE, Qingdao, pp 719–724
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
Jafari-Khouzani K, Soltanian-Zadeh H (2005) Radon transform orientation estimation for rotation invariant texture analysis. IEEE Trans Pattern Anal Mach Intell 27(6):1004–1008
Jain AK, Vailaya A (1996) Image retrieval using color and shape. Pattern Recogn 29(8):1233–1244
Jiang F, Hu H-M, Zheng J, Li B (2016) A hierarchal BoW for image retrieval by enhancing feature salience. Neurocomputing 175:146–154
Julesz B (1984) A brief outline of the texton theory of human vision. Trends Neurosci 7(2):41–45
Jyothi B, MadhaveeLatha Y, Mohan PK, Reddy V (2016) Steerable Texture Descriptor for an Effective Content-Based Medical Image Retrieval System Using PCA. In: 2016 International Conference on Computer and Communication Technologies. Springer, Heidelberg, pp 289–298
Kalantidis Y, Mellina C, Osindero S (2016) Cross-dimensional weighting for aggregated deep convolutional features. In: 2016 European Conference on Computer Vision. Springer, Heidelberg, pp 685–701
Kuhl FP, Giardina CR (1982) Elliptic Fourier features of a closed contour. Comput Graphics and Image Process 18(3):236–258
Lin C-H, Huang D-C, Chan Y-K, Chen K-H, Chang Y-J (2011) Fast color-spatial feature based image retrieval methods. Expert Syst Appl 38(9):11412–11420
Liu G-H, Yang J-Y (2008) Image retrieval based on the texton co-occurrence matrix. Pattern Recogn 41(12):3521–3527
Liu G-H, Yang J-Y (2013) Content-based image retrieval using color difference histogram. Pattern Recogn 46(1):188–198
Liu G-H, Zhang L, Hou Y-K, Li Z-Y, Yang J-Y (2010) Image retrieval based on multi-texton histogram. Pattern Recogn 43(7):2380–2389
Liu G-H, Li Z-Y, Zhang L, Xu Y (2011) Image retrieval based on micro-structure descriptor. Pattern Recogn 44(9):2123–2133
Liu G-H, Yang J-Y, Li Z (2015) Content-based image retrieval using computational visual attention model. Pattern Recogn 48(8):2554–2566
Liu Z, Wang S, Tian Q (2016) Fine-residual VLAD for image retrieval. Neurocomputing 173:1183–1191
Liu P, Guo J-M, Chamnongthai K, Prasetyo H (2017) Fusion of color histogram and LBP-based features for texture image retrieval and classification. Inf Sci 390:95–111
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110
Luo J, Crandall D (2006) Color object detection using spatial-color joint probability functions. IEEE Trans Image Process 15(6):1443–1453
Mahmoudi F, Shanbehzadeh J, Eftekhari-Moghadam A-M, Soltanian-Zadeh H (2003) Image retrieval based on shape similarity by edge orientation autocorrelogram. Pattern Recogn 36(8):1725–1736
Manjunath BS, Ohm J-R, Vasudevan VV, Yamada A (2001) Color and texture descriptors. IEEE Trans Circuits and Syst Video Technol 11(6):703–715
Manjunath BS, Salembier P, Sikora T (2002) Introduction to MPEG-7: multimedia content description interface. John Wiley & Sons, New York
Mehta P, Saoji L, Dodrajka A, Chug T (2016) Detection of Diseases on leaves and its possible diagnosis Using CBIR technique. International Education and Research Journal 2(2)
Mei T, Rui Y, Li S, Tian Q (2014) Multimedia search reranking: A literature survey. ACM Computing Surveys (CSUR) 46(3):38
Mezaris V, Kompatsiaris I, Strintzis MG (2003) An ontology approach to object-based image retrieval. In: 2003 I.E. International Conference on Image Processing, Vol. 2, IEEE, Barcelona, pp II-511
Nan B, Xu Y, Mu Z, Chen L (2015) Content-based image retrieval using local texture-based color histogram. In: 2015 I.E. International Conference on Cybernetics (CYBCONF). IEEE, Gdynia, pp 399–405
Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987
Palm C (2004) Color texture classification by integrative co-occurrence matrices. Pattern Recogn 37(5):965–976
Perronnin F, Liu Y, Sánchez J, Poirier H (2010) Large-scale image retrieval with compressed fisher vectors. In: 2010 I.E. Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, San Francisco, pp 3384–3391
Qi GJ, Hua XS, Rui Y, Mei T, Tang J, Zhang HJ (2007) Concurrent multiple instance learning for image categorization. In: 2007 I.E. Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Minneapolis, pp 1–8
Qian X, Hua X-S, Chen P, Ke L (2011) PLBP: An effective local binary patterns texture descriptor with pyramid representation. Pattern Recogn 44(10):2502–2515
Qian X, Zhao Y, Han J (2015) Image location estimation by salient region matching. IEEE Trans Image Process 24(11):4348–4358
Qian X, Wang H, Zhao Y, Hou X, Hong R, Wang M, Tang YY (2017) Image location inference by multisaliency enhancement. IEEE Transactions on Multimedia 19(4):813–821
Sajjad M, Ullah A, Ahmad J, Abbas N, Rho S, Baik S-W (2017) Integrating salient colors with rotational invariant texture features for image representation in retrieval systems. Multimedia Tools and Applications. 1–21. https://doi.org/10.1007/s11042-017-5010-5
Shrivastava N, Tyagi V (2014) Content based image retrieval based on relative locations of multiple regions of interest using selective regions matching. Inf Sci 259:212–224
Singh C, Kaur KP (2016) A fast and efficient image retrieval system based on color and texture features. J Vis Commun Image Represent 41:225–238
Srivastava P, Khare A (2017) Integration of wavelet transform, Local Binary Patterns and moments for content-based image retrieval. J Vis Commun Image Represent 42:78–103
Talib A, Mahmuddin M, Husni H, George LE (2013) A weighted dominant color descriptor for content-based image retrieval. J Vis Commun Image Represent 24(3):345–360
Tolias G, Sicre R, Jégou H (2015) Particular object retrieval with integral max-pooling of CNN activations. arXiv preprint arXiv:151105879
Tzelepi M, Tefas A (2018) Deep convolutional learning for Content Based Image Retrieval. Neurocomputing 275:2467–2478
Uricchio T, Ballan L, Seidenari L, Del Bimbo A (2017) Automatic image annotation via label transfer in the semantic space. Pattern Recog 71:144–157
Velmurugan K (2014) A survey of content-based image retrieval systems using scale-invariant feature transform (sift). International Journal of Advanced Re-search in Computer Science and Software Engineering 4(2):604–608
Wang X, Wang Z (2013) A novel method for image retrieval based on structure elements’ descriptor. J Vis Commun Image Represent 24(1):63–74
Wu J, Feng L, Liu S, Sun M (2017) Image retrieval framework based on texton uniform descriptor and modified manifold ranking. J Vis Commun Image Represent 49:78–88
Yang X, Qian X, Xue Y (2015) Scalable mobile image retrieval by exploring contextual saliency. IEEE Trans Image Process 24(6):1709–1721
Ng JYH, Yang F, Davis LS (2015) Exploiting local features from deep networks for image retrieval. arXiv preprint arXiv:1504.05133
Zeng S, Huang R, Wang H, Kang Z (2016) Image retrieval using spatiograms of colors quantized by Gaussian Mixture Models. Neurocomputing 171:673–684
Zhang S, Tian Q, Hua G, Huang Q, Gao W (2011) Generating descriptive visual words and visual phrases for large-scale image applications. IEEE Trans Image Process 20(9):2664–2677
Zhang S, Tian Q, Huang Q, Gao W, Rui Y (2013) Multi-order visual phrase for scalable image search. In: 2013 International Conference on Internet Multimedia Computing and Service. ACM, New York, pp 145–149
Zhao M, Zhang H, Sun J (2016) A novel image retrieval method based on multi-trend structure descriptor. J Vis Commun Image Represent 38:73–81
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Raza, A., Nawaz, T., Dawood, H. et al. Square texton histogram features for image retrieval. Multimed Tools Appl 78, 2719–2746 (2019). https://doi.org/10.1007/s11042-018-5795-x
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DOI: https://doi.org/10.1007/s11042-018-5795-x