Abstract
The dress of a person can provide information like culture, status, gender, and personality. Dress classification can help us improve e-commerce, image-based search engines for the fashion industry, video surveillance, and social media image categorization. Deep learning is emerging as one of the most powerful classification techniques in many fields like medicine, business, and science. In the fashion industry, Convolutional Neural Networks have played a vital role in dress identification classification, but it is still a difficult task due to cluttered backgrounds, different poses, and lack of fashion datasets with rich classes and annotations. To complement the small-sized datasets, transfer learning is being widely used in training deep learning models. Current research applies transfer learning in two steps on the InceptionV3 model pre-trained on the ImageNet dataset. First, the pre-trained model is fine-tuned on DeepFashion dataset to transfer the domain of learned parameters toward fashion. In the second step, the model is fine-tuned on Pak Dataset a collection of Asian cultural fashion images having cluttered backgrounds. Experiments show the robustness and usefulness of two-step transfer learning in the classification of fashion images having cluttered backgrounds. Dress classification can be used for fashion image retrieval systems and recommendation systems. Dress classification can also be used in video surveillance systems for finding missing persons or crime suspects.
Similar content being viewed by others
Data availability
The dataset generated in the implementation of current study is available from authors on request.
References
Cho H, Ah C, Min Yoo K, Seol J, Lee SG (2019) Leveraging class hierarchy in fashion classification, In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops. p 0–0
Hatcher WG, Yu W (2018) A survey of deep learning: platforms, applications and emerging research trends. IEEE Access 6:24411–24432
Hori, K, Okada S, and Nitta K (2016) Fashion Image Classification on Mobile Phones Using Layered Deep Convolutional Neural Networks, 15th International Conference on Mobile and Ubiquitous Multimedia (MUM ’16). p 359–361
Huang J, Feris RS, Chen Q, Yan S (2015) Cross-Domain Image Retrieval With a Dual Attribute-Aware Ranking Network, IEEE International Conference on Computer Vision (ICCV). p 1062–1070.
Iliukovich-Strakovskaia A, Dral A, Dral E (2016) Using Pre-Trained Models for Fine-Grained Image Classification in Fashion Field, First International Workshop on Fashion and KDD. p 31–40.
Inoue N, Simo-Serra E, Yamasaki T, Ishikawa H (2017) Multi-Label Fashion Image Classification with Minimal Human Supervision, IEEE International Conference on Computer Vision Workshop (ICCVW). p 2261–2267
Ioffe S, Szegedy C (2015) Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, International conference on machine learning. p 448–456.
Islam SS, Dey EK, Tawhid MNA, Hossain BM (2017) A CNN based approach for garments texture design classification. Adv Technol Inno 4:119–125
Jain S, Dhar J (2017) Image Based Search Engine Using Deep Learning, Tenth International Conference on Contemporary Computing (IC3). p 1–7.
Kayed M, Anter A, Mohamed H (2020) Classification of garments from fashion MNIST dataset using CNN LeNet-5 architecture, In 2020 International conference on innovative trends in communication and computer engineering (ITCE) IEEE. p 238–243
Lao B, Jagadeesh K (2016) Convolutional Neural Networks for Fashion Classification and Object Detection, CCCV 2015: Computer Vision. p 120–129
Li Y, Cao L, Zhu J, Luo J (2017) Mining fashion outfit composition using an end-to-end deep learning approach on set data. IEEE Transact Multimed 8:1946–1955
Liang X, Xu C, Shen X, Yang J, Liu S, Tang J, Lin L, Yan S (2015) Human Parsing With Contextualized Convolutional Neural Network, IEEE International Conference on Computer Vision (ICCV). p 1386–1394
Liang X, Shen X, Xiang D, Feng J, Lin L, Yan S (2016) Semantic Object Parsing With Local-Global Long Short-Term Memory, IEEE Conference on Computer Vision and Pattern Recognition (CVPR). p 3185–3193
Liu Z, Luo P, Qiu S, Wang X, Tang X (2016) DeepFashion: Powering Robust Clothes Recognition and Retrieval With Rich Annotations", IEEE Conference on Computer Vision and Pattern Recognition (CVPR). p 1096–1104
Moreu E, Martinelli A, Naughton M, Kelly P, Connor NE (2023) Fashion CUT: Unsupervised domain adaptation for visual pattern classification in clothes using synthetic data and pseudo-labels. In Image Analysis: 23rd Scandinavian Conference, SCIA 2023, Sirkka, Finland, April 18–21, 2023, Proceedings, Part I. p 314–324
Pawening RE, Dijaya R, Brian T, Suciati N (2015) Classification of Textile Image Using Support Vector Machine with Textural Feature, International Conference on Information & Communication Technology and Systems (ICTS). p 119–122
Rubio A, Yu L, Simo-Serra E, Moreno-Noguer F (2017) Multi-Modal Embedding for Main Product Detection in Fashion, IEEE International Conference on Computer Vision (ICCV). p 2236–2242
Schindler A, Lidy T, Karner S, Hecker M, (2018) Fashion and Apparel Classification using Convolutional Neural Networks, arXiv preprint arXiv:1811.04374
Seo Y, Shin KS (2018) Image Classification of Fine-grained Fashion Image Based on Style Using Pre-trained Convolutional Neural Network, IEEE 3rd International Conference on Big Data Analysis (ICBDA). p 387–390
Shin SY, Jo G, Wang G (2023) A novel method for fashion clothing image classification based on deep learning. J Inform Commun Technol 22(1):127–148
Simo-Serra E, Fidler S, Moreno-Noguer F, Urtasun R (2014) A High Performance CRF Model for Clothes Parsing, Asian Conference on Computer Vision (ACCV). p 64–81
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going Deeper With Convolutions, IEEE Conference on Computer Vision and Pattern Recognition (CVPR). p 1–9
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the Inception Architecture for Computer Vision, IEEE Conference on Computer Vision and Pattern Recognition (CVPR). p 2818–2826
Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning, Thirty-First AAAI Conference on Artificial Intelligence, San Francisco
Wazarkar S, Keshavamurthy BN (2018) Fashion image classification using matching points with linear convolution. Multimed Tools Applicat 77:1–18
Wu H, Gao Y, Guo X, Al-Halah Z, Rennie S, Grauman K, Feris R. (2021) Fashion iq: A new dataset towards retrieving images by natural language feedback, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. p 11307–11317
Yamaguchi K, Kiapour MH, Ortiz LE, Berg TL (2015) Retrieving similar styles to parse clothing. IEEE Transact Patt Anal Mach Intell 5:1028–1040
Yang W, Luo P, Lin L (2014) Clothing Co-Parsing by Joint Image Segmentation and Labeling, IEEE Conference on Computer Vision and Pattern Recognition (CVPR). p 3182–3189
Zhao B, Feng J, Wu X, Yan S (2017) Memory-Augmented Attribute Manipulation Networks for Interactive Fashion Search, IEEE Conference on Computer Vision and Pattern Recognition (CVPR). p 1520–1528
Funding
Current research does not involve any funding from any public, private, or other sectors.
Author information
Authors and Affiliations
Contributions
All authors participated in the current research study conception.
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare that there are no conflicts of interest regarding the publication of this paper.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Dilawar, M., Saleem, Y., Syed, I. et al. A framework for real-time dress classification in cluttered background images for robust image retrieval. Cogn Tech Work 25, 373–384 (2023). https://doi.org/10.1007/s10111-023-00735-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10111-023-00735-5