Square texton histogram features for image retrieval

Raza, Ahmad; Nawaz, Tabassam; Dawood, Hassan; Dawood, Hussain

doi:10.1007/s11042-018-5795-x

Cited by 23 publications

(15 citation statements)

References 65 publications

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“…We compare the MIFH method with the color volume histograms [4], local binary pattern (LBP) [11], multi-texton histogram (MTH) [6], histogram of oriented gradients (HOG) [23], and BOW histogram [25]. Besides, we also compare the MIFH method with some extend methods which are derived from our previous works (the multitexton histogram [6] and the micro-structure descriptor [8]), including CPV-THF [43], STH [44], and CMSD [45] methods. In the comparison experiment, the vector dimensionality for the MIFH, LBPH, BOW, MTH, and CVH methods are 102, 256, 1000, 82, and 104 bins, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…In the comparison experiment, the vector dimensionality for the MIFH, LBPH, BOW, MTH, and CVH methods are 102, 256, 1000, 82, and 104 bins, respectively. e vector dimensionality for the CPV-THF [43], STH [44], and CMSD [45] methods are 242, 172, and 88 bins, respectively. In the HOG descriptor, there are nine bins with a block size of three and a cell size of six.…”

Section: Resultsmentioning

confidence: 99%

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Image Retrieval Based on a Multi-Integration Features Model

Chu

Liu

2020

Mathematical Problems in Engineering

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Feature integration theory can be regarded as a perception theory, but the extraction of visual features using such a theory within the CBIR framework is a challenging problem. To address this problem, we extract the color and edge features based on a multi-integration features model and use these for image retrieval. A novel and highly simple but efficient visual feature descriptor, namely, a multi-integration features histogram, is proposed for image representation and content-based image retrieval. First, a color image is converted from the RGB to the HSV color space, and the color features and color differences are extracted. Then, the color differences are calculated to extract the edge features using a set of simple integration processes. Finally, combining the color, edge, and spatial layout features allows representing the image content. Experiments show that our method produces results comparable to existing and well-known methods on three datasets that contain 25,000 natural images. The performances are significantly better than that of the BOW histogram, local binary pattern histogram, histogram of oriented gradient, and multi-texton histogram, with performances similar to the color volume histogram.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Image Retrieval Based on a Multi-Integration Features Model

Chu

Liu

2020

Mathematical Problems in Engineering

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“…In order to validate the performance of the proposed method, some state-of-the-art methods were selected for comparison: SoC-GMM [55], LDP [56], SSH [1], CPV-THF [57], and STH [58]. A distance metric and benchmark dataset were required for the comparisons.…”

Section: Resultsmentioning

confidence: 99%

“…Here, we compare the proposed method with state-of-the-art methods such as SSH [1], SoC-GMM [55], LDP [56], CPV-THF [57], and STH [58].…”

Section: Performance Comparisonsmentioning

confidence: 99%

Image Retrieval Using the Intensity Variation Descriptor

Zhao

Liu

2020

Mathematical Problems in Engineering

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Variations between image pixel characteristics contain a wealth of information. Extraction of such cues can be used to describe image content. In this paper, we propose a novel descriptor, called the intensity variation descriptor (IVD), to represent variations in colour, edges, and intensity and apply it to image retrieval. The highlights of the proposed method are as follows. (1) The IVD combines the advantages of the HSV and RGB colour spaces. (2) It can simulate the lateral inhibition mechanism and orientation-selective mechanism to determine an optimal direction and spatial layout. (3) An extended weighted L1 distance metric is proposed to calculate the similarity of images. It does not require complex operations such as square or square root and leads to good performance. Comparative experiments on two Corel datasets containing 15,000 images show that the proposed method performs better than the SoC-GMM, CPV-THF, and STH methods and provides good matching of texture, colour, and shape.

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“…In early studies, low-level features of images were usually extracted to classify scenes, such as color, texture, and shape [17][18][19]. However, these methods based on low-level features have not been a hot topic in the field of scene classification due to its unsatisfactory classification effect.…”

Section: Motivationmentioning

confidence: 99%

Research on Indoor Scene Classification Mechanism Based on Multiple Descriptors Fusion

Qin

Pan

et al. 2020

Mobile Information Systems

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This study aims at the great limitations caused by the non-ROI (region of interest) information interference in traditional scene classification algorithms, including the changes of multiscale or various visual angles and the high similarity between classes and other factors. An effective indoor scene classification mechanism based on multiple descriptors fusion is proposed, which introduces the depth images to improve descriptor efficiency. The greedy descriptor filter algorithm (GDFA) is proposed to obtain valuable descriptors, and the multiple descriptor combination method is also given to further improve descriptor performance. Performance analysis and simulation results show that multiple descriptors fusion not only can achieve higher classification accuracy than principal components analysis (PCA) in the condition with medium and large size of descriptors but also can improve the classification accuracy than the other existing algorithms effectively.

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Square texton histogram features for image retrieval

Cited by 23 publications

References 65 publications

Image Retrieval Based on a Multi-Integration Features Model

Image Retrieval Based on a Multi-Integration Features Model

Image Retrieval Using the Intensity Variation Descriptor

Research on Indoor Scene Classification Mechanism Based on Multiple Descriptors Fusion

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