Abstract
With the invention of low cost image capturing devices, image acquisition is no longer a difficult task. The immense popularity of such devices has led to the production of large number of images. For accessing these images easily, efficient indexing and organization of images is required. The field of Content-Based Image Retrieval (CBIR) attempts to achieve this goal. This paper proposes a new multiresolution descriptor- Local Ternary Wavelet Gradient Pattern (LTWGP), for CBIR which combines shape feature and texture feature and utilizes this combination at multiple scales of image to construct feature vector for retrieval. Discrete Wavelet Transform (DWT) coefficients of grayscale image are computed followed by computation of Local Ternary Pattern (LTP) codes of resulting DWT coefficients. Finally, Histogram of Oriented Gradients (HOG) of resulting LTP codes is computed to construct feature vector. The advantage of this technique is that it computes texture through LTP which extracts complex structural arrangement of pixels more efficiently than other texture features such as Local Binary Pattern (LBP), and shape feature through HOG which measures shape of an object as a local feature without performing any segmentation operation. The combination of LTP and HOG is exploited at multiple resolutions of image through DWT to extract varying level of details so that the features left undetected at one level get detected at another level. The combination of LTP, HOG, and DWT constructs efficient feature descriptor which exploits multiple features at more than one resolution of image. The proposed feature descriptor efficiently extracts local directional information obtained through the combination of LTP and HOG at multiple levels of resolution decomposed through DWT. Performance of the proposed method is measured in terms of precision and recall and tested on four benchmark datasets, namely, Corel-1 K, Corel-5 K, Corel-10 K, and GHIM-10 K. The experimental results demonstrate that the proposed method outperforms other state-of-the-art CBIR techniques in terms of precision and recall.
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References
Agarwal M, Maheshwari RP (2012) Á trous gradient structure descriptor for content-based image retrieval. Int J Multimed Inf Retr 1(2):129–138
Bay H, Tinne T, Gool LV (2006) Surf: Speeded up robust features. In: Proceedings of European Conference on Computer Vision, Springer Berlin Heidelberg, pp 404–417
Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition vol 1, pp 886–893
Feng L, Wu J, Liu S, Zhang H (2015) Global correlation descriptor: a novel image representation for image retrieval. J Vis Commun Image Represent 33:104–114
Fu X, Li Y, Harrison R, Belkasim S (2006) Content-based image retrieval using gabor-zernike features. In: Proceedings of 18th International Conference on Pattern Recognition vol 2, pp 417–420
Gevers T, Smeulders AW (2000) Pictoseek: combining color and shape invariant features for image retrieval. IEEE Trans Image Process 9(1):102–119
Giveki D, Soltanshahi MA, Montazer GA (2017) A new image feature descriptor for content based image retrieval using scale invariant feature transform and local derivative pattern. Optik 131:242–254
http://wang.ist.psu.edu/docs/related/. Accessed April 2014
http://www.ci.gxnu.edu.cn/cbir/. Accessed June 2015
Junior OL, Delgado D, Gonçalves V, Nunes U (2009) Trainable classifier-fusion schemes: An application to pedestrian detection. In: Proceedings of 12th International IEEE Conference on Intelligent Transportation Systems, pp 1–6
Khare M, Srivastava RK, Khare A (2015) Moving object segmentation in Daubechies complex wavelet domain. SIViP 9:635–650
Liu G (2015) Content-based image retrieval based on visual attention and the conditional probability. In Proceedings of International Conference on Chemical, Material, and Food Engineering, Atlantis Press, pp 838–842
Liu G, Yang J (2013) Content-based image retrieval using color difference histogram. Pattern Recogn 46:188–198
Liu G, Zhang L, Hou Y, Yang J (2010) Image retrieval based on multi-texton histogram. Pattern Recogn 43(7):2380–2389
Liu G, Li Z, Zhang L, Xu Y (2011) Image retrieval based on micro-structure descriptor. Pattern Recogn 44(9):2123–2133
Liu GH, Yang JY, Li Z (2015) Content-based image retrieval using computational attention model. Pattern Recogn 48:2554–2566
Long F, Zhang H, Feng DD (2003) Multimedia information retrieval and management. Springer, Berlin, Heidelberg 1–26.
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110
Mallat S (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693
Manjunath BS, Ma WX (1996) Browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18(8):837–842
Ojala T, Pietikainen M, Harwood D (1996) A comparative study of texture measures with classification based on feature distributions. Pattern Recogn 29(1):51–59
Pass G, Zabih R (1996) Histogram refinement for content-based image retrieval. In: Proceedings of 3rd IEEE Workshop on Applications of Computer Vision, pp 96–102
Quellec G, Lamard M, Cazuguel G, Cochener B, Roux C (2012) Fast wavelet-based image characterization for highly adaptive image retrieval. IEEE Trans Image Process 21(4):1613–1623
Smith JR, Chang SF (1996) Tools and Techniques for Color Image Retreival. Storage and Retrieval for Still Image and Video Databases IV, International Society for Optics and Photonics 2670:426–437
Smith JR, Chang SF (1996) Tools and techniques for color image retrieval. Storage and Retrieval for Still Image and Video Databases IV vol 2670, pp 426–438
Smith JR, Chang SF (1997) VisualSEEk: a fully automated content-based image query system. In: Proceedings of the fourth ACM International Conference on Multimedia, pp 87–98
Srivastava P, Khare A (2016) On visual information retrieval using multiresolution techniques. In: Kumar AVS (ed) Web usage mining techniques and applications across industries. IGI Global, Hershey, pp 297–323
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
Srivastava P, Khare A (2017) Utilizing multiscale local binary pattern for content-based image retrieval. Multimed Tools Appl 77(10):12377–12403
Srivastava P, Khare A (2017) Content-Based Image Retrieval using Multiscale Local Spatial Binary Gaussian Co-occurrence Pattern. In: Proceedings of International Conference of Internet of Things for Technological Development, pp 85–95
Srivastava P, Khare A (2018) Content-based image retrieval using local binary curvelet co-occurrence pattern. Comput J 61(3):369–385
Srivastava P, Binh NT, Khare A (2014) Content-based image retrieval using moments of local ternary pattern. Mobile Netw Appl 19:618–625
Srivastava P, Binh NT, Khare A (2014) Content-based image retrieval using moments. In: Proceedings of 2nd International Conference on Context-Aware Systems and Applications, Phu Quoc, Vietnam, pp 228–237
Srivastava P, Prakash O, Khare A (2013) Content-Based Image Retrieval using Moments of Wavelet Transform. In: Proceedings of International Conference on Control Automation and Information Sciences, Gwangju, South Korea, pp 159–164
Starck J, Candes EJ, Donoho DL (2002) The Curvelet transform for image Denoising. IEEE Trans Image Process 11(6):670–684
Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19(6):1635–1650
Tiwari AK, Kanhangad V, Pachori RB (2017) Histogram refinement for texture descriptor based image retrieval. Signal Process Image Commun 53:73–85
Verma M, Balasubrahmanian R, Murala S (2015) Local Extrema Cooccurrence pattern using color and texture image retrieval. Neurocomputing 165:255–269
Wan J, Wang D, Hoi SC, Wu P, Zhu J, Zhang Y, Li J (2014) Deep learning for content-based image retrieval: A comprehensive study. In: Proceedings of the 22nd ACM international conference on Multimedia, pp 157–166 ACM
Wang JZ, Li J, Wiederhold G (2001) SIMPLIcity: semantics-sensitive integrated matching for picture libraries. IEEE Trans Pattern Anal Mach Intell 23(9):947–963
Yildizer E, Balci AM, Jarada TN, Alhajj R (2012) Integrating wavelets with clustering and indexing for effective content-based image retrieval. Knowl-Based Syst 31:55–66
Youssef SF (2012) ICTEDCT-CBIR: integrating curvelet transform with enhanced dominant colors extraction and texture analysis for efficient content-based image retrieval. Comput Electr Eng 38:1358–1376
Yu J, Qin Z, Wan T, Zhang X (2013) Feature integration analysis of bag-of-features model for image retrieval. Neurocomputing 120:355–364
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 D, Lu G (2002) Shape-based image retrieval using generic Fourier descriptor. Signal Process Image Commun 17(10):825–848
Zhang M, Zhang K, Feng Q, Wang J, Kong Jun LY (2014) A novel image retrieval method based on hybrid information descriptors. J Vis Commun Image Represent 25(7):1574–1587
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Srivastava, P., Khare, A. Content-based image retrieval using local ternary wavelet gradient pattern. Multimed Tools Appl 78, 34297–34322 (2019). https://doi.org/10.1007/s11042-019-08039-5
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DOI: https://doi.org/10.1007/s11042-019-08039-5