Learning Deep Similarity Models with Focus Ranking for Fabric Image Retrieval
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TLDR
This paper proposes a novel embedding method termed focus ranking that can be easily unified into a CNN for jointly learning image representations and metrics in the context of fine-grained fabric image retrieval and shows the superiority of the proposed model over existing metric embedding models.About:
This article is published in Image and Vision Computing.The article was published on 2018-02-01 and is currently open access. It has received 30 citations till now. The article focuses on the topics: Image retrieval & Feature learning.read more
Citations
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Journal ArticleDOI
Fabric Image Retrieval System Using Hierarchical Search Based on Deep Convolutional Neural Network
TL;DR: 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.
Journal ArticleDOI
Learning non-metric visual similarity for image retrieval
Noa Garcia,George Vogiatzis +1 more
TL;DR: It is argued that non-metric similarity functions based on neural networks can build a better model of human visual perception than standard metric distances.
Journal ArticleDOI
Image retrieval of wool fabric. Part I: Based on low-level texture features:
TL;DR: Experimental results indicate that the framework is effective and superior for image retrieval of wool fabric, providing referential assistance for the worker in the factory and improving retrieval efficiency.
Posted Content
Deep image retrieval: a survey
Wei Chen,Yu Liu,Weiping Wang,Erwin M. Bakker,Theodoros Georgiou,Paul Fieguth,Li Liu,Michael S. Lew +7 more
TL;DR: In this article, the authors organize and review recent content-based image retrieval (CBIR) works that are developed based on deep learning algorithms and techniques, including insights and techniques from recent papers.
Journal ArticleDOI
AutoRet: A Self-Supervised Spatial Recurrent Network for Content-Based Image Retrieval
Muhammad Mostafa Monowar,Md. Abdul Hamid,Abu Quwsar Ohi,Madini O. Alassafi,Muhammad F. Mridha +4 more
TL;DR: In this article , a self-supervised image retrieval system is proposed based on deep convolutional neural network (DCNN) for image retrieval, which can work in self-vision and can also be trained on a partially labeled dataset.
References
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Proceedings ArticleDOI
Multi-label Image Recognition by Recurrently Discovering Attentional Regions
TL;DR: In this paper, a spatial transformer layer is proposed to locate attentional regions from the convolutional feature maps in a region-proposal-free way and an LSTM (Long Short Term Memory) sub-network is used to sequentially predict semantic labeling scores on the located regions.
Proceedings ArticleDOI
A contextual dissimilarity measure for accurate and efficient image search
TL;DR: A contextual dissimilarity measure (CDM) takes into account the local distribution of the vectors and iteratively estimates distance correcting terms and is subsequently used to update an existing distance, thereby modifying the neighborhood structure.
Proceedings ArticleDOI
Fisher vectors meet Neural Networks: A hybrid classification architecture
Florent Perronnin,Diane Larlus +1 more
TL;DR: A hybrid architecture that combines their strengths: the first unsupervised layers rely on the FV while the subsequent fully-connected supervised layers are trained with back-propagation, which significantly outperforms standard FV systems without incurring the high cost that comes with CNNs.
Journal ArticleDOI
Deep Ranking for Person Re-Identification via Joint Representation Learning
TL;DR: A novel approach to person re-identification, a fundamental task in distributed multi-camera surveillance systems, is proposed that significantly outperforms all the state-of-the-art approaches, including both traditional and CNN-based methods on the challenging VIPeR, CUHK-01, and CAVIAR4REID datasets.