An Overview of Image Recognition and Retrieval of Clothing Items

Kashilani, Divva; Damahe, Lalit B.; Thakur, Nileshsingh V.

doi:10.1109/rice.2018.8509041

Cited by 6 publications

(5 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, reference (Chen et al, 2021) utilized the back-propagation (BP) neural networks for the identification of knitted fabrics, while another research (Foody and Mathur, 2004) employed text features and support vector machines (SVM) for recognizing knitted fabrics. Another approach, presented in reference (Kashilani et al, 2018), involved dividing clothing into 23 categories based on attitude estimation and features such as color and scale-invariant feature transform (SIFT). Furthermore, a system was developed to describe individuals' physical characteristics, including attributes like gender, T-shirt style and hair length (Bedeli et al, 2018).…”

Section: Related Work 21 Feature Representation Of Rgbd Imagesmentioning

confidence: 99%

Manifold embedded global and local discriminative features selection for single-shot multi-categories clothing recognition and retrieval

Huang

2023

IJICC

View full text Add to dashboard Cite

PurposeSingle-shot multi-category clothing recognition and retrieval play a crucial role in online searching and offline settlement scenarios. Existing clothing recognition methods based on RGBD clothing images often suffer from high-dimensional feature representations, leading to compromised performance and efficiency.Design/methodology/approachTo address this issue, this paper proposes a novel method called Manifold Embedded Discriminative Feature Selection (MEDFS) to select global and local features, thereby reducing the dimensionality of the feature representation and improving performance. Specifically, by combining three global features and three local features, a low-dimensional embedding is constructed to capture the correlations between features and categories. The MEDFS method designs an optimization framework utilizing manifold mapping and sparse regularization to achieve feature selection. The optimization objective is solved using an alternating iterative strategy, ensuring convergence.FindingsEmpirical studies conducted on a publicly available RGBD clothing image dataset demonstrate that the proposed MEDFS method achieves highly competitive clothing classification performance while maintaining efficiency in clothing recognition and retrieval.Originality/valueThis paper introduces a novel approach for multi-category clothing recognition and retrieval, incorporating the selection of global and local features. The proposed method holds potential for practical applications in real-world clothing scenarios.

show abstract

Section: Related Work 21 Feature Representation Of Rgbd Imagesmentioning

confidence: 99%

Manifold embedded global and local discriminative features selection for single-shot multi-categories clothing recognition and retrieval

Huang

2023

IJICC

View full text Add to dashboard Cite

show abstract

“…e development of the garment industry needs to follow the trend of the information age, closely link deep learning with the industry, and establish AI e-commerce and smart retail [15]. Based on the emerging deep learning technology, the computer is used to obtain clothing information, realize clothing identification, classification, retrieval, and collocation recommendation, so as to provide intelligent shopping services for consumers [16,17]. is happens through collecting large quantity of data and 2…”

Section: Recommendation Systemsmentioning

confidence: 99%

An Adaptive Recommendation Algorithm of Intelligent Clothing Design Elements Based on Large Database

Cui

2022

Mobile Information Systems

View full text Add to dashboard Cite

In the recent years, the developmental speed of intelligent technology continues to accelerate, and the research on the actual needs of users is also in depth. From the current situation of the clothing industry, how to combine artificial intelligence (AI) technology with clothing fashion has become the focus of customer’s attention. The application of intelligent clothing matching recommendation system (online) can effectively meet the needs of customers in dressing matching, so as to save a lot of time and energy (offline). With the maturity of artificial intelligence, machine learning, and other emerging computational technologies, the intelligent clothing matching system has laid a solid foundation. In this paper, several intelligent clothing matching recommendation systems that have been applied at present are deeply analyzed. Moreover, the basic algorithms and key technologies are elaborated in detail. In addition, the future research direction is found, so that the clothing matching recommendation system can be more personalized, and the comprehensive function is greatly improved in order to bring more ideal benefits.

show abstract

“…The papers in this group are review papers [33,65,98,11,134] that summarise the developments in fAshIon technology in different areas. Overview papers can help researchers to quickly understand the current situation of fAshIon research and familiarise them with the corresponding methods, related datasets, baseline approaches, and evaluation protocols.…”

Section: Overviewmentioning

confidence: 99%

“…Overview papers can help researchers to quickly understand the current situation of fAshIon research and familiarise them with the corresponding methods, related datasets, baseline approaches, and evaluation protocols. For example, [65] presents a summary of the various retrieval techniques used in clothing retrieval, and [11] provides an analysis of the four main tasks in fashion: fashion detection, including landmark detection, fashion parsing, and item retrieval; fashion analysis, consisting of attribute recognition, style learning, and popularity prediction; fashion synthesis, which involves style transfer, pose transformation, and physical simulation; and fashion recommendation, which comprises fashion compatibility, outfit matching, and hairstyle suggestion. [11] also presents the benchmark datasets and the evaluation protocols of each task.…”

Section: Overviewmentioning

confidence: 99%

fAshIon after fashion: A Report of AI in Fashion

Zou,

Wong

2021

Preprint

View full text Add to dashboard Cite

Sum ma ry dev elop me nts of tech nol ogy Ev al ua tio n pr ot oc ol s fo r sp ec ifi c ta sk s V is u a ll y u n d e rs ta n d in g o f fa sh io n im a g e s Key Points Detection Clothing Parsing Clothing Detection On lin e sel lin g Fashion Image Retrieval Attributes Recognition found ation for P e rs o n a l S t y li n g generated images category generated outfits fou nd ati on for color Ana lyze the tren ds and gett ing insi ght s for selli ng

show abstract

An Overview of Image Recognition and Retrieval of Clothing Items

Cited by 6 publications

References 24 publications

Manifold embedded global and local discriminative features selection for single-shot multi-categories clothing recognition and retrieval

Manifold embedded global and local discriminative features selection for single-shot multi-categories clothing recognition and retrieval

An Adaptive Recommendation Algorithm of Intelligent Clothing Design Elements Based on Large Database

fAshIon after fashion: A Report of AI in Fashion

Contact Info

Product

Resources

About