Learning deep similarity models with focus ranking for fabric image retrieval

Deng, Daiguo; Wang, Ruomei; Wu, Hefeng; He, Huayong; Li, Qi; Luo, Xiliang

doi:10.1016/j.imavis.2017.12.005

Cited by 31 publications

(20 citation statements)

References 43 publications

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“…According to [32] the focus ranking method can be integrated with CNN. Furthermore, the Alex-Net architecture is optimized by adding an auxiliary layer at the end of the FC to calculate the loss function and study the representation and metrics of images in fine-grained fabric image retrieval.…”

Section: Feature Extraction Based On Cnn Methodsmentioning

confidence: 99%

Content-based image retrieval for fabric images: A survey

Tena

Hartanto

Ardiyanto

2021

IJEECS

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In <span>recent years, a great deal of research has been conducted in the area of fabric image retrieval, especially the identification and classification of visual features. One of the challenges associated with the domain of content-based image retrieval (CBIR) is the semantic gap between low-level visual features and high-level human perceptions. Generally, CBIR includes two main components, namely feature extraction and similarity measurement. Therefore, this research aims to determine the content-based image retrieval for fabric using feature extraction techniques grouped into traditional methods and convolutional neural networks (CNN). Traditional descriptors deal with low-level features, while CNN addresses the high-level, called semantic features. Traditional descriptors have the advantage of shorter computation time and reduced system requirements. Meanwhile, CNN descriptors, which handle high-level features tailored to human perceptions, deal with large amounts of data and require a great deal of computation time. In general, the features of a CNN's fully connected layers are used for matching query and database images. In several studies, the extracted features of the CNN's convolutional layer were used for image retrieval. At the end of the CNN layer, hash codes are added to reduce </span>search time.

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Section: Feature Extraction Based On Cnn Methodsmentioning

confidence: 99%

Content-based image retrieval for fabric images: A survey

Tena

Hartanto

Ardiyanto

2021

IJEECS

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“…In their followup study, the average and maximum pooling of the last convolutional layer of CNN was taken as an image representation and better performance. Deng et al [44] present a method for fabric image retrieval based on learning deep similarity model with focus ranking. Perronnin and Larlus [27] proposed an image retrieval framework based on multi-feature fusion.…”

Section: B Deep Representation Learningmentioning

confidence: 99%

Fabric Image Retrieval System Using Hierarchical Search Based on Deep Convolutional Neural Network

et al. 2019

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Fabric image retrieval is a meaningful issue, due to its potential values in many areas such as textile product design, e-commerce, and inventory management. Meanwhile, it is challenging because of the diversity of fabric appearance. Encourage by the recent breakthrough in the deep convolutional neural network (CNN), a deep learning framework is applied for fabric image retrieval. The idea of the proposed framework is that the binary code and feature for representing the image can be learning by a deep CNN when the data labels are available. The proposed framework employs a hierarchical search strategy that includes coarse-level retrieval and fine-level retrieval. Otherwise, a large-scale wool fabric image retrieval dataset named WFID with about 20 000 images are built to validate the proposed framework. The longitudinal comparison experiments for self-parameter optimization and horizontal comparison experiments for verifying the superiority of the algorithm are performed on this data set. The comparison experimental results indicate the superiority of the proposed framework. INDEX TERMS Image retrieval, wool, fabric, feature extraction, machine learning, neural networks.

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“…In high-level image matching, deep learning techniques have been proposed to learn low-dimensional embedding spaces in face verification [8], retrieval [57,56], classification [20,39,35] and product search [4], either by using siamese [8] or triplet [56] architectures. More recently, deep similarity learning has also been applied to fabric image retrieval [9] by using triplets of samples to ensure that similar features are mapped closer than non-similar features.…”

Section: Related Workmentioning

confidence: 99%

Learning non-metric visual similarity for image retrieval

García

Vogiatzis

2019

Image and Vision Computing

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Measuring visual similarity between two or more instances within a data distribution is a fundamental task in image retrieval. Theoretically, non-metric distances are able to generate a more complex and accurate similarity model than metric distances, provided that the non-linear data distribution is precisely captured by the system. In this work, we explore neural networks models for learning a non-metric similarity function for instance search. We argue that non-metric similarity functions based on neural networks can build a better model of human visual perception than standard metric distances. As our proposed similarity function is differentiable, we explore a real end-to-end trainable approach for image retrieval, i.e. we learn the weights from the input image pixels to the final similarity score. Experimental evaluation shows that non-metric similarity networks are able to learn visual similarities between images and improve performance on top of state-of-the-art image representations, boosting results in standard image retrieval datasets with respect standard metric distances.

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Learning deep similarity models with focus ranking for fabric image retrieval

Cited by 31 publications

References 43 publications

Content-based image retrieval for fabric images: A survey

Content-based image retrieval for fabric images: A survey

Fabric Image Retrieval System Using Hierarchical Search Based on Deep Convolutional Neural Network

Learning non-metric visual similarity for image retrieval

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