Color Image Retrieval Algorithm Fusing Color and Principal Curvatures Information

Zhang, Haoxiang; Jiang, Man; Kou, Qiqi

doi:10.1109/access.2020.3030056

Cited by 9 publications

(9 citation statements)

References 45 publications

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“…Histogram of oriented gradients (HOG) is a powerful method that is robust to geometric invariance and image's optical deformation [46]. HOG is also robust to illumination variations in the images.…”

Section: Histogram Of Oriented Gradientsmentioning

confidence: 99%

Curvature Best Basis: A Novel Criterion to Dynamically Select a Single Best Basis as the Extracted Feature for Periocular Recognition

et al. 2022

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Aiming at the problems in the best basis selection, this paper presents a novel criterion based on the statistical measurement of the curvature wavelet coefficient to dynamically select the single best basis of the quad-tree wavelet packet transformation. The selected single best basis works as an extracted feature for biometric periocular recognition system. The proposed method first extracts the mean curvature of wavelet coefficients inside the quad-tree wavelet packet transform. Second, the method finds the most distinctive features based on the largest standard deviation and dynamically selects the extracted curvature wavelet coefficients as the single best basis. Third, the selected single curvature best basis works as an extracted feature, and then it is combined with the histogram of oriented gradients method. Finally, the support vector machine is employed to perform classification. Two datasets of two-dimensional periocular digital images are tested against the proposed method. To show the extended ability, we analyze the curvature best basis method against wavelet functions and characteristics and test the proposed method against the plain face and masked face recognition. The proposed method achieves the highest performance results inside periocular recognition (97.53% accuracy for UBIPr-1 and 97.77% accuracy for EYB-P1), masked face recognition (98.11% accuracy), and plain face recognition (98.26% accuracy). The proposed method is robust against glasses occlusion, artificial geometry transformations, Gaussian and salt pepper noise. Comparison with other works in a similar recognition system shows that our proposed curvature best basis method yields the highest performance results.INDEX TERMS Best basis, curvature, periocular recognition, wavelet packet transform.

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Section: Histogram Of Oriented Gradientsmentioning

confidence: 99%

Curvature Best Basis: A Novel Criterion to Dynamically Select a Single Best Basis as the Extracted Feature for Periocular Recognition

et al. 2022

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“…In Figure 5, the left boundary of G k and the right boundary of G (2) k overlap, and the left boundary of G k and the right boundary of G (3) k partially overlap.…”

Section: Grid Position Indexmentioning

confidence: 99%

“…where H o is the overlap height between the right edge of G (3) k and the left edge of G k and H k is the height of G k . Under each attribute index, the membership and nonmembership degrees of each grid are calculated by the aforementioned function, and the different numbers of Chinese character images are statistically analyzed with the average membership and average nonmembership degrees of three indexes.…”

Section: Grid Position Indexmentioning

confidence: 99%

“…e theoretical basis of image retrieval of Chinese characters in ancient books is content-based image retrieval [1,2]. e key link is feature extraction and matching, that is, color, texture, shape, and other features [3][4][5] or their fusion features [6] are extracted through mathematical descriptions, and similarity calculation is conducted based on image features to identify images that are similar. Traditional Chinese character feature extraction methods mainly adopt structural features [7] and statistical features [8].…”

Section: Introductionmentioning

confidence: 99%

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Ancient Chinese Character Image Retrieval Based on Dual Hesitant Fuzzy Sets

Yang

Tian

2021

Scientific Programming

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The complex and changeable structures of ancient Chinese characters result in the decreasing accuracy of their image retrieval. To resolve this problem, a new retrieval method based on dual hesitant fuzzy sets is proposed. Dual hesitation fuzzy sets that can express uncertain information more comprehensively are employed in the feature extraction process of directional line elements. The multiattribute evaluation index of adjacent grids for the current grid and its corresponding membership and nonmembership functions are established, and the weight of each attribute is calculated by the dual hesitation fuzzy entropy, such that the proposed features can fully reflect the topological structure of ancient Chinese characters. Using the dual hesitation fuzzy correlation coefficient to measure the similarity between the ancient Chinese character images to be retrieved and the candidate images, the retrieval of ancient Chinese character images is realized. Experiments show that when the t0hreshold value of the correlation coefficient is 0.9, the average retrieval accuracy is 90.4%.

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“…Because digital pictures are sampled in a 2D lattice, Jaggies are unavoidable. This issue cannot be overlooked in order to increase the quality of an expanded image since edges have a significant impact on the overall image quality [8].…”

Section: Introductionmentioning

confidence: 99%

Deep image retrieval using artificial neural network interpolation and indexing based on similarity measurement

Ahmad

2022

CAAI Trans on Intel Tech

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In content-based image retrieval (CBIR), primitive image signatures are critical because they represent the visual characteristics. Image signatures, which are algorithmically descriptive and accurately recognized visual components, are used to appropriately index and retrieve comparable results. To differentiate an image in the category of qualifying contender, feature vectors must have image information's like colour, objects, shape, spatial viewpoints. Previous methods such as sketch-based image retrieval by salient contour (SBIR) and greedy learning of deep Boltzmann machine (GDBM) used spatial information to distinguish between image categories. This requires interest points and also feature analysis emerged image detection problems. Thus, a proposed model to overcome this issue and predict the repeating pattern as well as series of pixels that conclude similarity has been necessary. In this study, a technique called CBIR-similarity measure via artificial neural network interpolation (CBIR-SMANN) has been presented. By collecting datasets, the images are resized then subject to Gaussian filtering in the pre-processing stage, then by permitting them to the Hessian detector, the interesting points are gathered. Based on Skewness, mean, kurtosis and standard deviation features were extracted then given to ANN for interpolation. Interpolated results are stored in a database for retrieval. In the testing stage, the query image was inputted that is subjected to pre-processing, and feature extraction was then fed to the similarity measurement function. Thus, ANN helps to get similar images from the database. CBIR-SMANN have been implemented in the python tool and then evaluated for its performance. Results show that CBIR-SMANN exhibited a high recall value of 78% with a minimum retrieval time of 980 ms. This showed the supremacy of the proposed model was comparatively greater than the previous ones.

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Color Image Retrieval Algorithm Fusing Color and Principal Curvatures Information

Cited by 9 publications

References 45 publications

Curvature Best Basis: A Novel Criterion to Dynamically Select a Single Best Basis as the Extracted Feature for Periocular Recognition

Curvature Best Basis: A Novel Criterion to Dynamically Select a Single Best Basis as the Extracted Feature for Periocular Recognition

Ancient Chinese Character Image Retrieval Based on Dual Hesitant Fuzzy Sets

Deep image retrieval using artificial neural network interpolation and indexing based on similarity measurement

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