Weighted-learning-instance-based retrieval model using instance distance

Wu, Hao; Li, Yueli; Xiong, Jie; Bi, Xiaohan; Zhang, Linna; Bie, Rongfang; Guo, Junqi

doi:10.1007/s00138-018-0988-x

Cited by 4 publications

(2 citation statements)

References 61 publications

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“…More importantly, the obvious spatial information of the image is often ignored as well. Some previous methods [29,30] consider the above problems to a certain extent, and they can also retrieve many semantically valid images. However, the above methods do not deeply mine the essential features of the image, and do not fully consider the spatial location information of the image.…”

Section: Introductionmentioning

confidence: 99%

Refined Sparse Representation Based Similar Category Image Retrieval

Wang¹,

Zhu²,

Hua³

2023

Computer Modeling in Engineering &Amp; Sciences

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Given one specific image, it would be quite significant if humanity could simply retrieve all those pictures that fall into a similar category of images. However, traditional methods are inclined to achieve high-quality retrieval by utilizing adequate learning instances, ignoring the extraction of the image's essential information which leads to difficulty in the retrieval of similar category images just using one reference image. Aiming to solve this problem above, we proposed in this paper one refined sparse representation based similar category image retrieval model. On the one hand, saliency detection and multi-level decomposition could contribute to taking salient and spatial information into consideration more fully in the future. On the other hand, the cross mutual sparse coding model aims to extract the image's essential feature to the maximum extent possible. At last, we set up a database concluding a large number of multi-source images. Adequate groups of comparative experiments show that our method could contribute to retrieving similar category images effectively. Moreover, adequate groups of ablation experiments show that nearly all procedures play their roles, respectively.

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Section: Introductionmentioning

confidence: 99%

Refined Sparse Representation Based Similar Category Image Retrieval

Wang¹,

Zhu²,

Hua³

2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

show abstract

“…For retrieval model construction, Bayesian model, Binary tree, K‐means, and SVM model are frequently used as classic retrieval models. On the foundation of those models, some improved models increase their capabilities.…”

Section: Introductionmentioning

confidence: 99%

Field of experts optimization‐based noisy image retrieval

et al. 2020

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The value of image retrieval has become more and more prominent in the era of big data. However, large numbers of images are missed from current method since the image retrieval precision largely depends on the high quality of images. By common methodology, when the quality of images decreases a little, the accuracy of retrieval would decrease significantly. In particular, it is difficult to retrieve noisy images effectively by conventional approach. Yet large number of the noisy images could not be ignored at the age of data explosion. Aiming at the problem above, we proposed noisy image retrieval model based on field of experts (FoE) optimization. High-quality learning images could be selected by sparse coding, which is based on similarity calculation model, and the multioption filter combination model enhances the power of FoE model. We set up a database containing a large numbers of noisy images. Over this database, adequate groups of experiments are conducted. The verification of the method concluded its effectiveness and superiority.

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Plug-and-Play video reconstruction using sparse 3D transform-domain block matching

Ghassab

Bouguila

2021

Machine Vision and Applications

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Weighted-learning-instance-based retrieval model using instance distance

Cited by 4 publications

References 61 publications

Refined Sparse Representation Based Similar Category Image Retrieval

Refined Sparse Representation Based Similar Category Image Retrieval

Field of experts optimization‐based noisy image retrieval

Plug-and-Play video reconstruction using sparse 3D transform-domain block matching

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