Learning Deep Similarity Models with Focus Ranking for Fabric Image Retrieval
Reads0
Chats0
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
More filters
Journal ArticleDOI
Structural textile pattern recognition and processing based on hypergraphs
TL;DR: In this paper, the authors introduce an approach for identifying similar weaving patterns based on their structures for textile archives using hypergraphs and extract multisets of k-neighbourhoods describing weaving patterns from these graphs.
Journal ArticleDOI
Wool fabric image retrieval based on soft similarity and listwise learning
TL;DR: A novel method for fabric image retrieval based on soft similarity and pairwise learning is presented, which has a greater improvement than the previous work.
Journal ArticleDOI
Methods and advancement of content-based fashion image retrieval: A Review
TL;DR: In this article , the authors categorized CBFIR methods into four main categories, i.e., image-guided CBFIR (with the addition of attributes and styles), image and text-guided, sketch-guided and video-guided.
Journal ArticleDOI
Deep Descriptor Learning with Auxiliary Classification Loss for Retrieving Images of Silk Fabrics in the Context of Preserving European Silk Heritage
TL;DR: This work addresses the problem of image retrieval for searching records in a database of silk fabrics by combiningcriptor learning with auxiliary classification loss with the aim of supporting the clustering in the descriptor space with respect to the properties of the depicted silk objects.
Journal ArticleDOI
Mélange fabric image retrieval based on soft similarity learning
Jun Xiang,Ruru Pan,Wei-Dong Gao +2 more
TL;DR: This work introduces a quantified similarity definition, soft similarity, to measure the fine-grained pairwise similarity and designs a CNN for fabric image representation and demonstrates that the proposed method outperforms the compared methods.
References
More filters
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Journal ArticleDOI
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.
Proceedings ArticleDOI
Fully convolutional networks for semantic segmentation
TL;DR: The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.
Proceedings ArticleDOI
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
TL;DR: RCNN as discussed by the authors combines CNNs with bottom-up region proposals to localize and segment objects, and when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost.