Abstract
The accuracy of content-based image retrieval (CBIR) systems is significantly affected by the discriminatory power of image features and distance measures. This paper performs an investigation towards finding the best local and global features and distance measures for content-based image retrieval. It provides insights into the trade-offs regarding computational costs, memory utilization and accuracy on several standard datasets which include MIRFLICKR, Corel, Holidays and ZuBuD. First, low-dimensional global and local features are extracted individually to generate a bank of small image features. Second, multilevel descriptor forms are utilized to produce highly discriminative image representations based on multi-features aggregation scheme. The relationship is highlighted between features (local and global) and other retrieval factors such as quantization approaches, visual codebooks, distance measures, vectorization methods, memory and retrieval speed. The resulting composite image representations are compact, i.e., only 32–64 vector dimension and 32–128 codebook size, and preserve high discriminative levels which further boost the retrieval accuracy and performance. The experimental results show that the presented multi-features image representations are efficient and outperform many competitive methods of the state-of-the-art.
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Alzu’bi A, Amira A, Ramzan N (2015) Semantic content-based image retrieval: a comprehensive study. J Vis Commun Image Represent 32:20–54
Li J, Allinson NM (2013) Relevance feedback in content-based image retrieval: a survey. In: Handbook on neural information processing. Springer Berlin, Heidelberg, pp 433–469
Datta R, Joshi D, Li J, Wang JZ (2008) Image retrieval: ideas, influences, and trends of the new age. ACM Comput Surv (CSUR) 40(2):5
Liu Y, Zhang D, Lu G, Ma WY (2007) A survey of content-based image retrieval with high-level semantics. Pattern Recognit 40(1):262–282
Duanmu X (2010) Image retrieval using color moment invariant. In: The seventh international conference on information technology: new generations (ITNG), 12–14, pp 200–203
Qiu G (2003) Color image indexing using BTC. IEEE Trans Image Process 12(1):93–101
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
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
Kwitt R, Uhl A (2010) Lightweight probabilistic texture retrieval. IEEE Trans Image Process 19(1):241–253
Lasmar NE, Berthoumieu Y (2014) Gaussian copula multivariate modeling for texture image retrieval using wavelet transforms. IEEE Trans Image Process 23(5):2246–2261
Haralick RM (1979) Statistical and structural approaches to texture. Proc IEEE 67(5):786–804
Tamura H, Mori S, Yamawaki T (1978) Textural features corresponding to visual perception. IEEE Trans Syst Man Cybern 8(6):460–473
Vogel J, Schiele B (2006) Performance evaluation and optimization for content-based image retrieval. Pattern Recognit 39(5):897–909
Lee J, Nang J (2011) Content-based image retrieval method using the relative location of multiple ROIs. Adv Electr Comput Eng 11(3):85–90
Hoàng N, Gouet-Brunet V, Rukoz M, Manouvrier M (2010) Embedding spatial information into image content description for scene retrieval. Pattern Recognit 43(9):3013–3024
Wang S, Liu D, Gu F, Feng Yang HL (2012) Similar matching for images with complex spatial relations. J Comput Inf Syst 8:8727–8734
Jaworska T, Kacprzyk J, Marín N, Zadrożny S (2010) On dealing with imprecise information in a content based image retrieval system. In: Computational intelligence for knowledge-based systems design. Springer, Berlin, Heidelberg, pp 149–158
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110
Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (SURF). Comput Vis Image Underst 110(3):346–359
Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE Computer Society conference on computer vision and pattern recognition, CVPR, vol 1, pp 886–893
Rosten E, Drummond T (2006) Machine learning for high-speed corner detection. In: Computer vision-ECCV, pp 430–443
Wang XY, Zhang BB, Yang HY (2014) Content-based image retrieval by integrating color and texture features. Multimed Tools Appl 68(3):545–569
Liu GH, Zhang L, Hou YK, Li ZY, Yang JY (2010) Image retrieval based on multi-texton histogram. Pattern Recognit 43(7):2380–2389
Huang ZC, Chan P, Ng W, Yeung DS (2010) Content-based image retrieval using color moment and Gabor texture feature. In: IEEE international conference on machine learning and cybernetics (ICMLC), vol 2, pp 719–724
Harris C, Stephens M (1988) A combined corner and edge detector. In: Alvey vision conference, vol 15, p 50
Matas J, Chum O, Urban M, Pajdla T (2002) Robust wide baseline stereo from maximally stable extremal regions. In: Proceedings of British machine vision conference, pp 384–393
Sivic J, Zisserman A (2003) Video Google: a text retrieval approach to object matching in videos. In: Ninth IEEE ICCV, pp 1470–1477
Ke Y, Sukthankar R (2004) PCA-SIFT: a more distinctive representation for local image descriptors. In: Proceedings of IEEE CVPR, vol 2, p II-506
Perronnin F, Dance C (2007) Fisher kernels on visual vocabularies for image categorization. In: IEEE CVPR’07, pp 1–8
Jégou H, Perronnin F, Douze M, Sanchez J, Perez P, Schmid C (2012) Aggregating local image descriptors into compact codes. IEEE Trans Pattern Anal Mach Intell 34(9):1704–1716
Iakovidou C, Anagnostopoulos N, Kapoutsis A, Boutalis Y, Lux M, Chatzichristofis SA (2015) Localizing global descriptors for content-based image retrieval. EURASIP J Adv Signal Process 1:1–20
ElAlami M (2014) A new matching strategy for content based image retrieval system. Appl Soft Comput 14:407–418
Zhang Y, Zhaoxing Z, Han X (2009) Category specific SIFT descriptor and its combination with color information for content-based image retrieval. In: Proceedings of the 2nd ACM international conference on interaction sciences: information technology, culture and human, pp 685–690
Deselaers T, Keysers D, Ney H (2008) Features for image retrieval: an experimental comparison. Inf Retr 11(2):77–107
Walia E, Verma V (2016) Boosting local texture descriptors with Log-Gabor filters response for improved image retrieval. Int J Multimed Inf Retr 5(4):173–184
Bosch A, Zisserman A, Munoz X (2007) Image classification using random forests and ferns. In: IEEE 11th ICCV, pp 1–8
Alzu’bi A, Amira A, Ramzan N, Jaber T (2015) Robust fusion of color and local descriptors for image retrieval and classification. In: IEEE international conference on systems, signals and image processing (IWSSIP), pp 253–256
Lee TS (1996) Image representation using 2D Gabor wavelets. IEEE Trans Pattern Anal Mach Intell 18(10):959–971
Mallat S (1998) A wavelet tour of signal processing. Academic Press, San Diego
Costa A F, Humpire-Mamani G,Traina A J (2012) An efficient algorithm for fractal analysis of textures. In: 25th IEEE SIBGRAPI, pp 39–46
Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987
Brahnam S, Jain LC, Nanni L, Lumini A (2014) Local binary patterns: new variants and applications. Springer, Berlin, Heidelberg
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
Bianconi F, Harvey R, Southam P, Fernández A (2011) Theoretical and experimental comparison of different approaches for color texture classification. J Electron Imaging 20(4):043006–043006
Mikolajczyk K, Schmid C (2004) Scale and affine invariant interest point detectors. Int J Comput Vis 60(1):63–86
Wang Z, Fan B, Wu F (2011) Local intensity order pattern for feature description. In: ICCV, pp 603–610
Arandjelović R, Zisserman A (2012) Three things everyone should know to improve object retrieval. In: IEEE CVPR, pp 2911–2918
Tola E, Lepetit V, Fua P (2010) An efficient dense descriptor applied to wide-baseline stereo. IEEE Trans Pattern Anal Mach Intell 32(5):815–830
Huiskes MJ, Thomee B, Lew MS (2010) New trends and ideas in visual concept detection: the MIR flickr retrieval evaluation initiative. I:n Multimedia information retrieval, pp 527– 536
Vedaldi A, Fulkerson B (2010) VLFeat: an open and portable library of computer vision algorithms. In: Proceedings of the 18th ACM conference on multimedia, pp 1469–1472
Jegou H, Douze M, Schmid C (2008) Hamming embedding and weak geometric consistency for large scale image search. In: ECCV, pp 304–317
Perronnin F, Liu Y, Sánchez J, Poirier H (2010) Large-scale image retrieval with compressed fisher vectors. In: Proceedings of CVPR, pp 3384–3391
Arandjelovic R, Zisserman A (2013) All about VLAD. In: CVPR, pp 1578–1585
Delhumeau J, Gosselin P H, Jégou H, Pérez P (2013) Revisiting the VLAD image representation. In: ACM multimedia, pp 653–656
Shao H, Svoboda T, Van Gool L (2003) Zubud-zurich buildings database for image based recognition. Technical report 260, Computer Vision Lab, Swiss Federal Institute of Technology, Switzerland
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Alzu’bi, A., Amira, A., Ramzan, N. et al. Improving content-based image retrieval with compact global and local multi-features. Int J Multimed Info Retr 5, 237–253 (2016). https://doi.org/10.1007/s13735-016-0109-4
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DOI: https://doi.org/10.1007/s13735-016-0109-4