Abstract
Due to recent development in technology, the complexity of multimedia is significantly increased and the retrieval of similar multimedia content is an open research problem. In the service of multimedia service, the requirement of Multimedia Indexing Technology is increasing to retrieve and search for interesting data from huge Internet. Since the traditional retrieval method, which is using textual index, has limitation to handle the multimedia data in current Internet, alternatively, the more efficient representation method is needed. Content-Based Image Retrieval (CBIR) is a process that provides a framework for image search and low-level visual features are commonly used to retrieve the images from the image database. The basic requirement in any image retrieval process is to sort the images with a close similarity in term of visual appearance. The color, shape, and texture are the examples of low-level image features. The feature combination that is also known as feature fusion is applied in CBIR to increase the performance, a single feature is not robust to the transformations that are in the image datasets. This paper represents a new Content-Based Image Retrieval (CBIR) technique to fuse the color and texture features to extract local features as our feature vector. The features are created for each image and stored as a feature vector in the database. The proposed research is divided into three phases that feature extraction, similarities match, and performance evaluation. Color Moments (CM) are used for Color features and extract the Texture features, used Gabor Wavelet and Discrete Wavelet transform. To enhance the power of feature vector representation, Color and Edge Directivity Descriptor (CEDD) is also included in the feature vector. We selected this combination, as these features are reported intuitive, compact and robust for image representation. We evaluated the performance of our proposed research by using the Corel, Corel-1500, and Ground Truth (GT) images dataset. The average precision and recall measures are used to evaluate the performance of the proposed research. The proposed approach is efficient in term of feature extraction and the efficiency and effectiveness of the proposed research outperform the existing research in term of average precision and recall values.
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References
Afifi AJ, Ashour WM (2012) Content-based image retrieval using invariant color and texture features. In: 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA). IEEE, pp 1–6
Agarwal S, Verma A, Dixit N (2014) Content based image retrieval using color edge detection and discrete wavelet transform. In: 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT). IEEE, pp 368–372
Ahmad J, Sajjad M, Mehmood I, Baik SW (2015) Ssh: Salient structures histogram for content based image retrieval. In: 2015 18th International Conference on Network-Based Information Systems (NBis). IEEE, pp 212–217
Ahmad J, Sajjad M, Mehmood I, Rho S, Baik SW (2015) Describing colors, textures and shapes for content based image retrieval-a survey. arXiv:150207041
Ashraf R, Ahmed M, Jabbar S, Khalid S, Ahmad A, Din S, Jeon G (2018) Content based image retrieval by using color descriptor and discrete wavelet transform. J Med Syst 42(3):44
Ashraf R, Bajwa KB, Mahmood T (2016) Content-based image retrieval by exploring bandletized regions through support vector machines. J Inf Sci Eng 32 (2):245–269
Ashraf R, Bajwa KB, Mahmood T (2016) Content-based image retrieval by exploring bandletized regions through support vector machines. J Inf Sci Eng 32 (2):245–269
Ashraf R, Bashir K, Irtaza A, Mahmood MT (2015) Content based image retrieval using embedded neural networks with bandletized regions. Entropy 17 (6):3552–3580
Ashraf R, Mahmood T, Irtaza A, Bajwa K (2014) A novel approach for the gender classification through trained neural networks. J Basic Appl Sci Res 4:136–144
Bu H-H, Kim N-c, Moon C-J, Kim J-H (2017) Content-based image retrieval using combined color and texture features extracted by multi-resolution multi-direction filtering. J Inf Process Syst 13(3):464–475
Chatzichristofis S, Boutalis Y (2007) A hybrid scheme for fast and accurate image retrieval based on color descriptors. In: IASTED international conference on artificial intelligence and soft computing (ASC 2007), Spain
Chatzichristofis SA, Boutalis YS (2008) Cedd: color and edge directivity descriptor: a compact descriptor for image indexing and retrieval. In: International Conference on Computer Vision Systems Springer, pp 312–322
Chatzichristofis SA, Zagoris K, Boutalis YS, Papamarkos N (2010) Accurate image retrieval based on compact composite descriptors and relevance feedback information. Int J Pattern Recognit Artif Intell 24(02):207–244
Datta R, Joshi D, Li J, Wang JZ (2008) Image retrieval: Ideas, influences, and trends of the new age. ACM Comput Surv 40(2):5
ElAdel A, Ejbali R, Zaied M, Amar CB (2016) A hybrid approach for content-based image retrieval based on fast beta wavelet network and fuzzy decision support system. Mach Vis Appl 27(6):781–799
ElAlami ME (2014) A new matching strategy for content based image retrieval system. ApplSoft Comput 14:407–418
Fakheri M, Sedghi T, Shayesteh MG, Amirani MC (2013) Framework for image retrieval using machine learning and statistical similarity matching techniques. IET Image Proc 7(1):1–11
Farhan M, Aslam M, Jabbar S, Khalid S, Kim M (2017) Real-time imaging-based assessment model for improving teaching performance and student experience in e-learning. Journal Real-Time Image Processing, pp 1–14
Irtaza A, Jaffar MA (2014) Categorical image retrieval through genetically optimized support vector machines (gosvm) and hybrid texture features Signal, Image and Video Process, pp 1–17
Kokare M, Chatterji BN, Biswas PK (2004) Cosine-modulated wavelet based texture features for content-based image retrieval. Pattern Recognit Lett 25(4):391–398
Lieberman H, Rosenzweig E, Singh P (2001) Aria: An agent for annotating and retrieving images. Computer 34(7):57–62
Lin C-H, Chen R-T, Chan Y-K (2009) A smart content-based image retrieval system based on color and texture feature. Image Vision Comput 27(6):658–665
Liu G-H (2015) Content-based image retrieval based on visual attention and the conditional probability. In: International Conference on Chemical, Material, and Food Engineering, Atlantis Press, pp 838–842
Liu G-H, Yang J-Y (2013) Content-based image retrieval using color difference histogram. Pattern Recognit 46(1):188–198
Pavithra L, Sharmila TS (2017) An efficient framework for image retrieval using color, texture and edge features Comput Electr Eng
Piras L, Giacinto G (2017) Information fusion in content based image retrieval: A comprehensive overview. Inf Fusion 37:50–60
Pujari J, Hiremath P (2007) Content based image retrieval based on color texture and shape features using image and its complement. Int J Comput Sci Secur 1(4):25–35
Sankar SP, Vishwanath N et al. (2017) An effective content based medical image retrieval by using abc based artificial neural network (ann). Current Med Imag Rev 13 (3):223–230
Shah DM, Desai U (2017) A survey on combine approach of low level features extraction in cbir. In: 2017 International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). IEEE, pp 284–289
Shleymovich M, Medvedev M, Lyasheva SA (2017) Image analysis in unmanned aerial vehicle on-board system for objects detection and recognition with the help of energy characteristics based on wavelet transform. In: XIV International Scientific and Technical Conference on Optical Technologies in Telecommunications International Society for Optics and Photonics, pp 1034210–1034210
Singh H, Agrawal D (2016) A meta-analysis on content based image retrieval system. In: Emerging Technological Trends (ICETT), International Conference on IEEE, pp 1–6
Singh VP, Srivastava R (2017) Improved image retrieval using color-invariant moments. In: 2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT). IEEE, pp 1–6
Srivastava P, Khare A (2017) Integration of wavelet transform, local binary patterns and moments for content-based image retrieval. J Visual Commun Image Represent 42:78–103
Stejić Z, Takama Y, Hirota K (2003) Genetic algorithm-based relevance feedback for image retrieval using local similarity patterns. Inf Process Manag 39(1):1–23
Tian X, Jiao L, Liu X, Zhang X (2014) Feature integration of eodh and color-sift: Application to image retrieval based on codebook. Signal Process Image Commun 29(4):530–545
Tzelepi M, Tefas A (2016) Relevance feedback in deep convolutional neural networks for content based image retrieval. In: Proceedings of the 9th Hellenic Conference on Artificial Intelligence ACM, p 27
Upadhyaya N, Dixit M (2016) A novel approach for cbir using color strings with multi-fusion feature method. Digital Image Proc 8(5):137–145
Varish N, Pradhan J, Pal AK (2017) Image retrieval based on non-uniform bins of color histogram and dual tree complex wavelet transform. Multimedia Tools Appl 76 (14):15885–15921
Wan J, Wang D, Hoi SCH, 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 ACM, pp 157–166
Wang X-Y, Yang H-Y, Li D-M (2013) A new content-based image retrieval technique using color and texture information. Comput Electr Eng 39(3):746–761
Wei G, Cao H, Ma H, Qi S, Qian W, Ma Z (2018) Content-based image retrieval for lung nodule classification using texture features and learned distance metric. J Med Syst 42(1):13
Won CS, Park DK, Park S-J (2002) Efficient use of mpeg-7 edge histogram descriptor. ETRI J 24(1):23–30
Yalavarthi A, Veeraswamy K, Sheela KA (2017) Content based image retrieval using enhanced gabor wavelet transform. In: 2017 International Conference on Computer, Communications and Electronics (Comptelix). IEEE, pp 339–343
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
Yu J, Qin Z, Wan T, Zhang X (2013) Feature integration analysis of bag-of-features model for image retrieval. Neurocomputing 120:355–364
Yue J, Li Z, Liu L, Fu Z (2011) Content-based image retrieval using color and texture fused features. Math Comput Modell 54(3):1121–1127
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, Islam MM, Lu G (2012) A review on automatic image annotation techniques. Pattern Recognit 45(1):346–362
Zhao M, Zhang H, Meng L (2016) An angle structure descriptor for image retrieval. China Commun 13(8):222–230
Zheng L, Yang Y, Tian Q (2017) Sift meets cnn: A decade survey of instance retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence
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Ashraf, R., Ahmed, M., Ahmad, U. et al. MDCBIR-MF: multimedia data for content-based image retrieval by using multiple features. Multimed Tools Appl 79, 8553–8579 (2020). https://doi.org/10.1007/s11042-018-5961-1
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DOI: https://doi.org/10.1007/s11042-018-5961-1