Uyghur Printed Document Image Retrieval Based on SIFT Features

Batur, Aliya; Tursun, Gulzira; Mamut, Mutellip; Yadikar, Nurbiya; Ubul, Kurban

doi:10.1016/j.procs.2017.03.157

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…Compared to a local featurebased target search, the global feature-based target search method is relatively simple and computationally fast, but it is ambiguous, which means the semantic meanings expressed by images with similar features may be different, thus leading to a lower accuracy of the target search. The common method based on local features is the scale-invariant feature transform (SIFT) method [18], which generates 128-dimensional feature vectors for each key point. Meanwhile, the SIFT feature vectors are invariant to image scaling and rotation, with robustness to affine transformations, noise interference, and luminance transformations.…”

Section: Target Search Methodsmentioning

confidence: 99%

Target Search for Joint Local and High-Level Semantic Information Based on Image Preprocessing Enhancement in Indoor Low-Light Environments

Tang,

Qin,

Yang

et al. 2023

IJGI

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In indoor low-light environments, the lack of light makes the captured images often suffer from quality degradation problems, including missing features in dark areas, noise interference, low brightness, and low contrast. Therefore, the feature extraction algorithms are unable to extract the feature information contained in the images accurately, thereby hindering the subsequent target search task in this environment and making it difficult to determine the location information of the target. Aiming at this problem, a joint local and high-level semantic information (JLHS) target search method is proposed based on joint bilateral filtering and camera response model (JBCRM) image preprocessing enhancement. The JBCRM method improves the image quality by highlighting the dark region features and removing the noise interference in order to solve the problem of the difficult extraction of feature points in low-light images, thus providing better visual data for subsequent target search tasks. The JLHS method increases the feature matching accuracy between the target image and the offline database image by combining local and high-level semantic information to characterize the image content, thereby boosting the accuracy of the target search. Experiments show that, compared with the existing image-enhancement methods, the PSNR of the JBCRM method is increased by 34.24% at the highest and 2.61% at the lowest. The SSIM increased by 63.64% at most and increased by 12.50% at least. The Laplacian operator increased by 54.47% at most and 3.49% at least. When the mainstream feature extraction techniques, SIFT, ORB, AKAZE, and BRISK, are utilized, the number of feature points in the JBCRM-enhanced images are improved by a minimum of 20.51% and a maximum of 303.44% over the original low-light images. Compared with other target search methods, the average search error of the JLHS method is only 9.8 cm, which is 91.90% lower than the histogram-based search method. Meanwhile, the average search error is reduced by 18.33% compared to the VGG16-based target search method. As a result, the method proposed in this paper significantly improves the accuracy of the target search in low-light environments, thus broadening the application scenarios of target search in indoor environments, and providing an effective solution for accurately determining the location of the target in geospatial space.

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Section: Target Search Methodsmentioning

confidence: 99%

Target Search for Joint Local and High-Level Semantic Information Based on Image Preprocessing Enhancement in Indoor Low-Light Environments

Tang,

Qin,

Yang

et al. 2023

IJGI

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“…Hence, the textual units were disassembled into sections and part‐based finding has done, which employs local gradient features from object recognition fields, SIFT to define these structures. A retrieval scheme for document images based on SIFT functions has been proposed in [18] for printed Uyghur document images. The strength of the SIFT features basic elements and the number of incorrect matching points in the SIFT feature to succeed local feature‐based image manuscript retrieval for 1000 Uyghur printed manuscript images with a different threshold for statistics.…”

Section: Related Workmentioning

confidence: 99%

SIFT and HOG features for the retrieval of ancient Kannada epigraphs

2020

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In this work, the authors presented the indexing method for efficient retrieval of Kannada characters. Here the characters were segmented by the connected component method. Then features were extracted by scale invariant feature transform (SIFT) and histogram of oriented gradients (HOG) methods. These features were condensed by principal component analysis. They presented the indexing approach using K‐dimensional tree (K‐D tree) to improve the identification process. For the experiment, they used their own database. The results of the experiment show that the indexing prior is faster than conventional identification approaches in terms of time to script. From experimentation, they observe that fusion features achieve the maximum accuracy of 90% with varying principal component analysis features.

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