Abstract
Texture-based instance retrieval is typically performed on images that present a single texture pattern and is mainly applied to the retrieval of fabrics or textiles. In this work, we apply it to indoor scene images that typically present many different texture patterns, which constitutes a more challenging problem. Such retrieval systems, together with the retrieval of faces and objects, can be used as a valuable tool for evidence matching in crime scene investigation. Even though recent deep learning-based approaches have made significant improvement in many computer vision tasks, texture retrieval remains an open problem. In this work, we introduce a Fourier-based approach, in which spatial images and their discrete Fourier transform maps are combined to derive a novel texture representation. We further present a new and efficient texture-based image retrieval framework based on region proposal networks, convolutional autoencoders and transfer learning, in which we extract the features from the latent space layer of the encoder as texture descriptors. The experimental results on four datasets: TextileTube, Outex, USPtex and Stex, validated the effectiveness of our proposed method, yielding better results than the current state of the art.
Similar content being viewed by others
References
Saritha RR, Paul V, Kumar PG (2019) Content based image retrieval using deep learning process. Cluster Comput 22(2):4187–4200
Tzelepi M, Tefas A (2018) Deep convolutional learning for content based image retrieval. Neurocomputing 275:2467–2478
Ahmed KT, Ummesafi S, Iqbal A (2019) Content based image retrieval using image features information fusion. Inf Fusion 51:76–99
Alzu’bi A, Abuarqoub A (2020) Deep learning model with low-dimensional random projection for large-scale image search. Eng Sci Technol Int J 23:911–920
Forcen JI, Pagola M, Barrenechea E, Bustince H (2020) Co-occurrence of deep convolutional features for image search. Image Vis Comput 97:103909
Keisler R, Skillman SW, Gonnabathula S, Poehnelt J, Rudelis X, Warren MS (2019) Visual search over billions of aerial and satellite images. Comput Vis Image Underst 187:102790102790
Saikia S, Fidalgo E, Alegre E, Fernández-Robles L (2017) Object detection for crime scene evidence analysis using deep learning. In: International conference on image analysis and processing, Springer, pp 14–24
Karie NM, Kebande VR, Venter H (2019) Diverging deep learning cognitive computing techniques into cyber forensics. Forensic Sci Int Synergy 1:61–67
Mohammad RMA, Alqahtani M (2019) A comparison of machine learning techniques for file system forensics analysis. J Inf Secur Appl 46:53–61
Liu W, Wu CY (2019) Crime scene investigation image retrieval using a hierarchical approach and rank fusion. In: 2019 14th IEEE conference on industrial electronics and applications (ICIEA), IEEE, pp 1974–1978
Liu Y, Hu D, Fan J, Wang F, Zhang D (2017) Multi-feature fusion for crime scene investigation image retrieval. In: 2017 international conference on digital image computing: techniques and applications (DICTA), IEEE, pp 1–7
Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, pp 91–99
He K, Gkioxari G, Dollár P, Girshick R (2017) Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 2961–2969
Nanni L, Brahnam S, Lumini A (2010) A local approach based on a local binary patterns variant texture descriptor for classifying pain states. Expert Syst Appl 37(12):7888–7894
Nanni L, Lumini A, Brahnam S (2010) Local binary patterns variants as texture descriptors for medical image analysis. Artif intell Med 49(2):117–125
Srinivasan G, Shobha G (2008) Statistical texture analysis. Proc World Acad Sci Eng Technol 36:1264–1269
Van de Wouwer G, Scheunders P, Van Dyck D (1999) Statistical texture characterization from discrete wavelet representations. IEEE Trans Image Process 8(4):592–598
Wu Q, Wang J, Yang C, Cui G, Yang W (2016) Target recognition by texture segmentation algorithm. Expert Syst Appl 46:394–404
Zheng L, Yang Y, Tian Q (2017) Sift meets cnn: a decade survey of instance retrieval. IEEE Trans Pattern Anal Mach Intell 40(5):1224–1244
Singh B, Li H, Sharma A, Davis LS (2018) R-fcn-3000 at 30fps: Decoupling detection and classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1081–1090
Tian Z, Shen C, Chen H, He T (2019) Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp 9627–9636
Liu L, Ouyang W, Wang X, Fieguth P, Chen J, Liu X, Pietikäinen M (2020) Deep learning for generic object detection: a survey. Int J Comput Vis 128(2):261–318
Wu X, Sahoo D, Hoi SC (2020) Recent advances in deep learning for object detection. Neurocomputing 396:39–64
Ma W, Wu Y, Cen F, Wang G (2020) Mdfn: Multi-scale deep feature learning network for object detection. Pattern Recognit 100:107149
Babenko A, Slesarev A, Chigorin A, Lempitsky V (2014) Neural codes for image retrieval. In: European conference on computer vision, Springer, pp 584–599
Ng WW, Li J, Tian X, Wang H, Kwong S, Wallace J (2020) Multi-level supervised hashing with deep features for efficient image retrieval. Neurocomputing 399:171–182
Wu Y, Wang S, Huang Q (2019) Multi-modal semantic autoencoder for cross-modal retrieval. Neurocomputing 331:165–175
Daoud MI, Saleh A, Hababeh I, Alazrai R (2019) Content-based image retrieval for breast ultrasound images using convolutional autoencoders: A feasibility study. In: 2019 3rd International Conference on Bio-engineering for Smart Technologies (BioSMART), IEEE, pp 1–4
Xu G, Fang W (2016) Shape retrieval using deep autoencoder learning representation. In: 2016 13th international computer conference on wavelet active media technology and information processing (ICCWAMTIP), IEEE, pp 227–230
Ying G, Wei Q, Shen X, Han S (2008) A two-step phase-retrieval method in fourier-transform ghost imaging. Opt Commun 281(20):5130–5132
Sokic E, Konjicija S (2016) Phase preserving fourier descriptor for shape-based image retrieval. Signal Process Image Commun 40:82–96
Tsai DM, Tseng CF (1999) Surface roughness classification for castings. Pattern Recognit 32(3):389–405
Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, Ieee, pp 248–255
Dai J, Li Y, He K, Sun J (2016) R-fcn: Object detection via region-based fully convolutional networks. In: Advances in neural information processing systems, pp 379–387
Alzu’bi A, Amira A, Ramzan N (2015) Semantic content-based image retrieval: a comprehensive study. J Vis Commun Image Rep 32:20–54
García-Olalla O, Alegre E, Fernández-Robles L, Fidalgo E, Saikia S (2018) Textile retrieval based on image content from cdc and webcam cameras in indoor environments. Sensors 18(5):1329
Kwitt R, Uhl A (2008) Image similarity measurement by kullback-leibler divergences between complex wavelet subband statistics for texture retrieval. In: 2008 15th IEEE international conference on image processing, IEEE, pp 933–936
Ouslimani F, Ouslimani A, Ameur Z (2019) Rotation-invariant features based on directional coding for texture classification. Neural Comput Appl 31(10):6393–6400
Pham MT (2018) Efficient texture retrieval using multiscale local extrema descriptors and covariance embedding. In: Proceedings of the european conference on computer vision (ECCV)
Tuncer T, Dogan S, Ertam F (2019) A novel neural network based image descriptor for texture classification. Phys A Stat Mech Appl 526:120955
Tuncer T, Dogan S, Ataman V (2019) A novel and accurate chess pattern for automated texture classification. Phys A Stat Mech Appl 536:122584
King I, Lau TK (1996) A feature-based image retrieval database for the fashion, textile, and clothing industry in hong kong. In: Proceedings of international symposium multi-technology information processing, vol 96, pp 233–240
D’Amato JP, Mercado M, Heiling A, Cifuentes V (2016) A proximal optimization method to the problem of nesting irregular pieces using parallel architectures. Rev Iberoam de Autom Inform Ind 13(2):220–227
Wong C (2017) Applications of computer vision in fashion and textiles. Woodhead Publishing, Cambridge
Gordo A, Almazan J, Revaud J, Larlus D (2017) End-to-end learning of deep visual representations for image retrieval. Int J Comput Vis 124(2):237–254
Dos Santos JM, De Moura ES, Da Silva AS, da Silva Torres R (2017) Color and texture applied to a signature-based bag of visual words method for image retrieval. Multimed Tools Appl 76(15):16855–16872
Mezaris V, Kompatsiaris I, Strintzis MG (2003) An ontology approach to object-based image retrieval. In: Proceedings 2003 international conference on image processing (Cat. No. 03CH37429), IEEE, vol 2, pp II–511
Ren J, Shen Y, Guo L (2003) A novel image retrieval based on representative colors. In: Proceedings of the Image and Vision Computing, NZ, Citeseer
Song J, Gao L, Liu L, Zhu X, Sebe N (2018) Quantization-based hashing: a general framework for scalable image and video retrieval. Pattern Recognit 75:175–187
Maillot N, Thonnat M, Boucher A (2004) Towards ontology-based cognitive vision. Mach Vis Appl 16(1):33–40
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: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), IEEE, vol 1, pp 886–893
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
Verma M, Raman B (2018) Local neighborhood difference pattern: a new feature descriptor for natural and texture image retrieval. Multimed Tools Appl 77(10):11843–11866
Nan B, Xu Y, Mu Z, Chen L (2015) Content-based image retrieval using local texture-based color histogram. In: 2015 IEEE 2nd international conference on cybernetics (CYBCONF), IEEE, pp 399–405
Singh C, Walia E, Kaur KP (2018) Color texture description with novel local binary patterns for effective image retrieval. Pattern Recognit 76:50–68
Pavithra L, Sharmila TS (2018) An efficient framework for image retrieval using color, texture and edge features. Comput Electrl Eng 70:580–593
Yang C, Yu Q (2019) Multiscale fourier descriptor based on triangular features for shape retrieval. Signal Process Image Commun 71:110–119
Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE (2017) A survey of deep neural network architectures and their applications. Neurocomputing 234:11–26
Popa CA, Cernăzanu-Glăvan C (2018) Fourier transform-based image classification using complex-valued convolutional neural networks. In: International symposium on neural networks, Springer, pp 300–309
Chitsaz K, Hajabdollahi M, Karimi N, Samavi S, Shirani S (2020) Acceleration of convolutional neural network using fft-based split convolutions. arXiv preprint arXiv:200312621
Saikia S, Fidalgo E, Alegre E, Fernández-Robles L (2017) Query based object retrieval using neural codes. International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017. Springer, Proceeding, pp 513–523
Salvador A, Giró-i Nieto X, Marqués F, Satoh S (2016) Faster r-cnn features for instance search. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 9–16
Cimpoi M, Maji S, Kokkinos I, Mohamed S, Vedaldi A (2014) Describing textures in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3606–3613
Cimpoi M, Maji S, Vedaldi A (2015) Deep filter banks for texture recognition and segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3828–3836
Cimpoi M, Maji S, Kokkinos I, Vedaldi A (2016) Deep filter banks for texture recognition, description, and segmentation. Int J Comput Vis 118(1):65–94
Yikun Y, Shengjie J, Jinrong H, Bisheng X, Jiabo L, Ru X (2020) Image retrieval via learning content-based deep quality model towards big data. Future Gener Comput Syst 112:243–249
Redmon J, Farhadi A (2018) Yolov3: An incremental improvement. arXiv preprint arXiv:180402767
Abdelmounaime S, Dong-Chen H (2013) New brodatz-based image databases for grayscale color and multiband texture analysis. ISRN Machine Vision 2013
Casanova D, Florindo JB, Falvo M, Bruno OM (2016) Texture analysis using fractal descriptors estimated by the mutual interference of color channels. Inf Sci 346:58–72
Kwitt R, Meerwald P (2018) Salzburg texture image database (stex)
Guo JM, Prasetyo H, Wang NJ (2015) Effective image retrieval system using dot-diffused block truncation coding features. IEEE Trans Multimed 17(9):1576–1590
Napoletano P (2017) Hand-crafted vs learned descriptors for color texture classification. In: International workshop on computational color imaging, Springer, pp 259–271
Acknowledgements
This work has been supported by the grant Junta de Castilla y Leon (EDU/529/2017) and the framework agreement between the University of Leon and INCIBE (Spanish National Cybersecurity Institute) under Addendum 01. We gratefully acknowledge the support of Nvidia Corporation for their kind donation of GPUs (GeForce GTX Titan X and K-40).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
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
Saikia, S., Fernández-Robles, L., Alegre, E. et al. Image retrieval based on texture using latent space representation of discrete Fourier transformed maps. Neural Comput & Applic 33, 13301–13316 (2021). https://doi.org/10.1007/s00521-021-05955-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-021-05955-2