Abstract
The enormous increase in digital image collections motivates the research community to propose powerful Content Based Image Retrieval (CBIR) algorithms to employ in critical scientific domains. In this paper, we have proposed Tetragonal Local Octa-Patterns for CBIR that are based on the direction of center pixel and generate an 8-bit octa-pattern. Neighbors at three diagonal locations are then used to generate Tetragonal Octa-Patterns. In order to enhance the precision, Genetic algorithm has been applied on obtained features to resolve the class imbalance problem for better classification through SVM. Experimental results prove the reliability of method by comparing against state-of-the-art methods in terms of precision and recall.
Similar content being viewed by others
References
Arevalillo-Herr’aez M, Ferri FJ, Moreno-Picot S (2011) Distance-based relevance feedback using a hybrid inter active genetic algorithm for image retrieval. Appl Soft Comput 11(2):1782–1791
Bay H, Ess A, Tuytelaars T, Gool LV (2008) Speeded-up robust features. Comput Vis Image Underst 110(3):346–359
Caltech-101 dataset. Online available on: https://www.vision.caltech.edu/Image_Datasets/Caltech101. Accessed 13 Dec 2016
Caltech-256 dataset. Online available on: http://www.vision.caltech.edu/Image_Datasets/Caltech256. Accessed 13 Dec 2016
Campana BJL, Keogh EJ (2010) A compression-based distance measure for texture. Statist Anal Data Mining 3(6):381–398
Cinque L, Ciocca G, Levialdi S, Pellicano A, Schettini R (2001) Color based image retrieval using spatial chromatic histograms. Image Vis Comput 19(13):979–986
ElAlami ME (2011) A novel image retrieval model based on the most relevant features. Knowl-Based Syst 24:23–32
ElAlami ME (2013) New matching strategy for content based image retrieval system. Appl Soft Comput 14:407–418
Freeman WT, Adelson EH (1991) The design and use of steerable filters. IEEE Trans Pattern Anal Mach Intell 13(9):891–906
Galar M, Fernandez A, Barrenechea E, Bustince H, Herrera F (2012) A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Trans Syst Man Cybern Part C Appl Rev 42(4):463–484
Guha T, Ward RK (2014) Image similarity using sparse representation and compression distance. IEEE Trans Multimedia 16(4):980–987
Guo JM, Prasetyo H (2015) Content-based image retrieval using features extracted from Halftoning based block truncation coding. IEEE Trans Image Process 24(3):1010–1024
Harris C, Stephens M (1988) A combined corner and edge detector. In: Alvey vision Conf., pp 147–151
Hearst MA et al (1998) Support vector machines. IEEE Intelligent Systems and their Applications 13(4):18–28
Herrera F, Lozano M, Sanchez A (2005) Hybrid crossover operators for real-coded genetic algorithms : an experimental study. Soft Comput 9:280–298
Huang PW, Dai SK (2003) Image retrieval by texture similarity. Pattern Recogn 36(3):665–679
Irtaza A, Jaffar MA (2014) Categorical image retrieval through genetically optimized support vector machines (GOSVM) and hybrid texture features. SIViP 9(7):1503–1519
Irtaza A, Jaffar MA et al (2014) Embedding neural networks for semantic association in content based image retrieval. Multimed Tools Appl 72(2):1911–1931
Jain AK, Vailaya A (1996) Image retrieval using color and shape. Pattern Recogn 29(8):1,233–1,244
Jhanwar N, Chaudhuri S, Seetharaman G, Zavidovique B (2004) Content based image retrieval using motif co-occurrence matrix. Image Vis Comput 22:1211–1220
Jin C, Ke S-W (2017) Content-based image retrieval based on shape similarity calculation. 3D Res 8:23
Lai C-C, Chen Y-C (2011) A user-oriented image retrieval system based on interactive genetic algorithm. IEEE Trans Instrum Meas 60:3318–3325
Lin C-H, Chen R-T, Chan Y-K (2009) A smart content-based image retrieval system based on color and texture feature. Image Vis Comput 27:658–665
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110
Manjunath BS, Ma W-Y (1996) Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18(8):837–842
Mao J, Jain AK (1992) Texture classification and segmentation using multiresolution simultaneous autoregressive models. Pattern Recogn 25(2):173–188
Mehmood Z, Abbas F, Mahmood T, Javid MA, Rehman A, Nawaz T (2018) Content-based image retrieval based on visual words fusion versus features fusion of local and global features. Arab J Sci Eng:1–20
Murala S, Maheshwari RP, Balasubramanian R (2012) Local tetra patterns: a new feature descriptor for content-based image retrieval. IEEE Trans Image Process 21(5):2874–2886
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
Oliva A, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vis 42(3):145–175
Oxford Flowers Dataset. Online available on: http://www.robots.ox.ac.uk/∼vgg/data/flowers/17/. Accessed 13 Dec 2016
Pass G, Zabih R, Miller J (1997) Comparing images using color coherence vectors. In: Proceedings of the fourth ACM international conference on multimedia. https://doi.org/10.1145/244130.244148
Raveaux R, Burie JC, Ogier JM (2013) Structured representations in a content based image retrieval context. J Vis Commun Image Represent 24:1252–1268
Swain MJ, Ballard DH (1991) Color indexing. Int J Comput Vis 7(1):11–32
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
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
Yang X, Cai L (2014) Adaptive region matching for region-based image retrieval by constructing region importance index. IET Comput Vis 8(2):141–151
Youssef SM (2012) ICTEDCT-CBIR integrating curvelet transform with enhanced dominant colors extraction and texture analysis for efficient content based image retrieval. Comput Electr Eng 38(5):1358–1376
Zhang B, Gao Y, Zhao S, Liu J (2010) Local derivative pattern versus local binary pattern: face recognition with higher-order local pattern descriptor. IEEE Trans Image Process 19(2):533–544
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Shabbir, Z., Irtaza, A., Javed, A. et al. Tetragonal Local Octa-Pattern (T-LOP) based image retrieval using genetically optimized support vector machines. Multimed Tools Appl 78, 23617–23638 (2019). https://doi.org/10.1007/s11042-019-7597-1
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-019-7597-1