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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Deep metric learning using Triplet network
Elad Hoffer,Nir Ailon +1 more
TL;DR: In this paper, Wang et al. proposed the triplet network model, which aims to learn useful representations by distance comparisons, and demonstrate using various datasets that their model learns a better representation than that of its immediate competitor, the Siamese network.
Journal ArticleDOI
Improving Bag-of-Features for Large Scale Image Search
TL;DR: A more precise representation based on Hamming embedding (HE) and weak geometric consistency constraints (WGC) is derived and this approach is shown to outperform the state-of-the-art on the three datasets.
Proceedings ArticleDOI
Person re-identification by probabilistic relative distance comparison
TL;DR: A novel Probabilistic Relative Distance Comparison (PRDC) model is introduced, which differs from most existing distance learning methods in that it aims to maximise the probability of a pair of true match having a smaller distance than that of a wrong match pair, which makes the model more tolerant to appearance changes and less susceptible to model over-fitting.
Proceedings ArticleDOI
Discriminative Deep Metric Learning for Face Verification in the Wild
Junlin Hu,Jiwen Lu,Yap-Peng Tan +2 more
TL;DR: The proposed DDML trains a deep neural network which learns a set of hierarchical nonlinear transformations to project face pairs into the same feature subspace, under which the distance of each positive face pair is less than a smaller threshold and that of each negative pair is higher than a larger threshold.
Proceedings ArticleDOI
PCCA: A new approach for distance learning from sparse pairwise constraints
Alexis Mignon,Frédéric Jurie +1 more
TL;DR: PCCA learns a projection into a low-dimensional space where the distance between pairs of data points respects the desired constraints, exhibiting good generalization properties in presence of high dimensional data.