Perceptual uniform descriptor and ranking on manifold for image retrieval

Liu, Shenglan; Wu, Jun; Feng, Lin; Qiao, Hong; Liu, Yang; Luo, Wenbo; Wang, Wei

doi:10.1016/j.ins.2017.10.010

Cited by 29 publications

(19 citation statements)

References 47 publications

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“…The experimental result validates that the fusion of color and texture information enhances the performance of image retrieval [29]. According to Liu et al [30], the ranking and incompatibility of the image feature descriptor is not considered much in the domain of image retrieval. The authors address the problem of incompatibility by using gestalt psychology theory and manifold learning.…”

Section: Related Worksupporting

confidence: 60%

“…A combination of gradient direction and color is used to imitate human visual uniformity. The selection of a proposed feature scheme [30] enhances the image retrieval performance. According to Wu et al [31], ranking and feature representation are two important factors that can enhance the performance of image retrieval and they are considered separately in image retrieval models.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR

et al. 2018

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The requirement for effective image search, which motivates the use of Content-Based Image Retrieval (CBIR) and the search of similar multimedia contents on the basis of user query, remains an open research problem for computer vision applications. The application domains for Bag of Visual Words (BoVW) based image representations are object recognition, image classification and content-based image analysis. Interest point detectors are quantized in the feature space and the final histogram or image signature do not retain any detail about co-occurrences of features in the 2D image space. This spatial information is crucial, as it adversely affects the performance of an image classification-based model. The most notable contribution in this context is Spatial Pyramid Matching (SPM), which captures the absolute spatial distribution of visual words. However, SPM is sensitive to image transformations such as rotation, flipping and translation. When images are not well-aligned, SPM may lose its discriminative power. This paper introduces a novel approach to encoding the relative spatial information for histogram-based representation of the BoVW model. This is established by computing the global geometric relationship between pairs of identical visual words with respect to the centroid of an image. The proposed research is evaluated by using five different datasets. Comprehensive experiments demonstrate the robustness of the proposed image representation as compared to the state-of-the-art methods in terms of precision and recall values.

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Section: Related Worksupporting

confidence: 60%

Section: Related Workmentioning

confidence: 99%

A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR

et al. 2018

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show abstract

“…In order to improve the robustness against illumination, the image normalization [60] will be exploited in the image pre-processing. In addition, manifold learning (ML) [61,62] and query expansion (QE) [63] will also be considered to further enhance the retrieval performance.…”

Section: Discussionmentioning

confidence: 99%

Local Ternary Cross Structure Pattern: A Color LBP Feature Extraction with Applications in CBIR

Feng

Wei

et al. 2019

Applied Sciences

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With the advent of medical endoscopes, earth observation satellites and personal phones, content-based image retrieval (CBIR) has attracted considerable attention, triggered by its wide applications, e.g., medical image analytics, remote sensing, and person re-identification. However, constructing effective feature extraction is still recognized as a challenging problem. To tackle this problem, we first propose the five-level color quantizer (FLCQ) to acquire a color quantization map (CQM). Secondly, according to the anatomical structure of the human visual system, the color quantization map (CQM) is amalgamated with a local binary pattern (LBP) map to construct a local ternary cross structure pattern (LTCSP). Third, the LTCSP is further converted into the uniform local ternary cross structure pattern (LTCSPuni) and the rotation-invariant local ternary cross structure pattern (LTCSPri) in order to cut down the computational cost and improve the robustness, respectively. Finally, through quantitative and qualitative evaluations on face, objects, landmark, textural and natural scene datasets, the experimental results illustrate that the proposed descriptors are effective, robust and practical in terms of CBIR application. In addition, the computational complexity is further evaluated to produce an in-depth analysis.

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“…And four feature extraction methods are applied on each dataset. They are HSV [14], MSD [15], CDH [16], PUD [17].…”

Section: Dataset and Methodsmentioning

confidence: 99%

Local Stretch Similarity Measure and Auto Query Expansion for Re-Ranking of Image Retrieval

Lin¹,

Luo²,

Liu³

et al. 2018

dtcse

Self Cite

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Using local neighbors (contextual information) to measure similarities between images is an effective re-ranking method in image retrieval, which has many advantages (e.g., low time complexity, easy promotion for new images). However, traditional methods usually cannot take neighbors into account comprehensively for the influence of noises (the more neighbors chosen, the more noises involved). To solve this problem, we propose Local Stretch Similarity Measure algorithm (LSSM). LSSM chooses multiple layers of neighbors to measure similarities, through which more contextual information can be considered and less noises will be introduced. Furthermore, we propose Auto Query Expansion (AQE) re-ranking method to transform the original single-query problem to a multi-query problem. By means of AQE, the robustness of LSSM can be enhanced. Extensive experiments are conducted on Corel-1K, Corel-10K and UK-bench datasets. Experimental results validate that our methods outperform other state-of-the-art methods.

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Perceptual uniform descriptor and ranking on manifold for image retrieval

Cited by 29 publications

References 47 publications

A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR

A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR

Local Ternary Cross Structure Pattern: A Color LBP Feature Extraction with Applications in CBIR

Local Stretch Similarity Measure and Auto Query Expansion for Re-Ranking of Image Retrieval

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