Fine-Grained Classification of Product Images Based on Convolutional Neural Networks

Liu, Tongtong; Wang, Rubing; Chen, Jikang; Han, Shengliang; Yang, Jimin

doi:10.4236/ami.2018.84007

Cited by 8 publications

(6 citation statements)

References 14 publications

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“…However, these classification methods cannot cover all clothing styles, because the same style of clothing also has different styles of pattern and material composition. The formation of a style is precisely the combination of fine-grained attributes [25,26], e.g., gothic, baroque or bohemian styles, which can be visually distinguished, but some styles of clothing are difficult to distinguish only by vision. For example, simple style, street style and Korean style are overlapped in some attributes.…”

Section: Discover Stylementioning

confidence: 99%

High-Accuracy Clothing and Style Classification via Multi-Feature Fusion

et al. 2022

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In recent years, the online selection of virtual clothing styles has been used to explore and expand diversified personal aesthetics, and it is also an overall reform and challenge to the clothing industry. Under the condition of the existing clothing style categories, this paper puts forward a style classification method combining fine-grained and coarse-grained techniques. Furthermore, a new deep neural network is proposed, which can improve the robustness of recognition and avoid the interference of image background through the pan learning and the background learning of image features. In order to study the relationship between the fine-grained attributes of clothing and the whole style, firstly, the clothing types are learned to realize the pre-training of model parameters. Secondly, through the transfer learning of the first stage of the pre-training model parameters, the model parameters are fine-tuned to make them more suitable for identifying the coarse-grained style types. Finally, a network structure based on the dual attention mechanism is proposed to improve the accuracy of final identification by adding different attention mechanisms at different stages of the network to enhance the performance of network features. In the experiment, we collected 50,000 images of 10 clothing styles to train and evaluate the models. The results show that the proposed classification method can effectively distinguish clothing styles and types.

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Section: Discover Stylementioning

confidence: 99%

High-Accuracy Clothing and Style Classification via Multi-Feature Fusion

et al. 2022

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“…The R-CNN [12] model proposed by Girshick et al, which made a great breakthrough in target detection. By experiment, mean Average Precision(mAP) for all categories of R-CNN on VOC 2007 data set [13] increased to 58.5 %.…”

Section: R-cnnmentioning

confidence: 99%

Target Detection Based on Deep Learning

Zhu

2022

J. Phys.: Conf. Ser.

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As an important research direction of computer vision, target detection has been widely used in face recognition, intelligent driving, robot navigation and other fields. In recent years, with the deepening research on deep learning, great progress has been made in the field of computer vision, such as image acquisition, image processing and target detection. Compared with the traditional target detection algorithm based on candidate regions, it has the problems of poor timeliness and slow detection speed. Recently, the popular target detection algorithm based on regression realizes the real sense of end-to-end detection and greatly improves the detection efficiency. However, the accuracy of small target detection and dense target detection has not been solved. In the future, we still need to improve the efficiency and accuracy of recognition on the existing basis, and solve the problem of small target and dense target detection to make it more widely used in practical application scenarios. In this paper, the principle, advantages and disadvantages, accuracy and other aspects of the above algorithms are introduced in detail, the problems existing in the target detection algorithm are summarized, and the future development direction has been prospected. In short, both algorithms have advantages and disadvantages, but the regression-based target detection algorithm has better practicality and development prospects.

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“…The standard problem of image classification exists also in specialized variants for products. This is driven by needs for efficient product image classification methods, and state-of-the-art solutions are typically based on supervised methods [30]. Like in generic image classification, convolutional neural networks have lead to largest advances.…”

Section: Product and Fashion Classificationmentioning

confidence: 99%

On augmenting database schemas by latent visual attributes

2021

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Decision-making in our everyday lives is surrounded by visually important information. Fashion, housing, dating, food or travel are just a few examples. At the same time, most commonly used tools for information retrieval operate on relational and text-based search models which are well understood by end users, but unable to directly cover visual information contained in images or videos. Researcher communities have been trying to reveal the semantics of multimedia in the last decades with ever-improving results, dominated by the success of deep learning. However, this does not close the gap to relational retrieval model on its own and often rather solves a very specialized task like assigning one of pre-defined classes to each object within a closed application ecosystem. Retrieval models based on these novel techniques are difficult to integrate in existing application-agnostic environments built around relational databases, and therefore, they are not so widely used in the industry. In this paper, we address the problem of closing the gap between visual information retrieval and relational database model. We propose and formalize a model for discovering candidates for new relational attributes by analysis of available visual content. We design and implement a system architecture supporting the attribute extraction, suggestion and acceptance processes. We apply the solution in the context of e-commerce and show how it can be seamlessly integrated with SQL environments widely used in the industry. At last, we evaluate the system in a user study and discuss the obtained results.

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Fine-Grained Classification of Product Images Based on Convolutional Neural Networks

Cited by 8 publications

References 14 publications

High-Accuracy Clothing and Style Classification via Multi-Feature Fusion

High-Accuracy Clothing and Style Classification via Multi-Feature Fusion

Target Detection Based on Deep Learning

On augmenting database schemas by latent visual attributes

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