Journal ArticleDOI
Image retrieval of wool fabric. Part I: Based on low-level texture features:
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TLDR
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.Abstract:
Color is difficult to distinguish by human vision and is described by keywords, resulting in low efficiency of wool fabric retrieval in factories at present. To obtain the process sheets of existin...read more
Citations
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Fabric Retrieval Based on Multi-Task Learning
TL;DR: Wang et al. as discussed by the authors proposed a novel approach for fabric image retrieval based on multi-task learning and deep hashing, which is a special case in Content Based Image Retrieval (CBIR).
Journal ArticleDOI
A novel image retrieval strategy based on transfer learning and hand-crafted features for wool fabric
TL;DR: In this paper , a novel image retrieval strategy was proposed to discriminate appearance differences by the classification model based on transfer learning and realize further discrimination delicate differences by hand-crafted features, which can provide referential assistance for the production crew and reducing manual labor in the factory.
Journal ArticleDOI
Patterned fabric image retrieval using relevant feedback via geometric similarity
TL;DR: A novel approach for patterned fabric retrieval based on the non-subsampled contourlet transform (NSCT) feature descriptor and relevance feedback technique is proposed and can significantly improve the performance of the retrieval system compared to other state-of-the-art algorithms.
Journal ArticleDOI
A novel image retrieval strategy based on transfer learning and hand-crafted features for wool fabric
TL;DR: In this paper, a novel image retrieval strategy was proposed to discriminate appearance differences by the classification model based on transfer learning and realize further discrimination delicate differences by hand-crafted features, which can provide referential assistance for the production crew and reducing manual labor in the factory.
Journal ArticleDOI
Image retrieval of wool fabric. Part III: based on aggregated convolutional descriptors and approximate nearest neighbors search:
TL;DR: Experimental results indicate that the proposed method can combine the texture and color feature, being effective and superior for image retrieval of wool fabric.
References
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Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
TL;DR: A generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis.
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Content-based image retrieval at the end of the early years
TL;DR: The working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap are discussed, as well as aspects of system engineering: databases, system architecture, and evaluation.
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Color and texture descriptors
TL;DR: An overview of color and texture descriptors that have been approved for the Final Committee Draft of the MPEG-7 standard is presented, explained in detail by their semantics, extraction and usage.
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A survey of content-based image retrieval with high-level semantics
TL;DR: This paper attempts to provide a comprehensive survey of the recent technical achievements in high-level semantic-based image retrieval, identifying five major categories of the state-of-the-art techniques in narrowing down the 'semantic gap'.
Journal ArticleDOI
The Fast Fourier Transform and Its Applications
TL;DR: A description of the alogorithm and its programming is given here and followed by a theorem relating its operands, the finite sample sequences, to the continuous functions they often are intended to approximate.