Content-based Image Retrieval Considering Colour Difference Histogram of Image Texture and Edge Orientation

AlyanNezhadi, Mohammad. M.; Qazanfari, Hamed; Ajam, A.; Amiri, Zahra

doi:10.5829/ije.2020.33.05b.28

Cited by 2 publications

(2 citation statements)

References 33 publications

(47 reference statements)

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“…Uniform Local Binary Pattern (uniform-LBP) [30] is used to extract texture features from ROI (non-ROI areas are set to zero). LBP is a famous method [31] [32] for feature extraction that is used in many WCE abnormality detection methods [33,34]. In the LBP algorithm, eight-pixels with a radius of one around the pixel are considered as the neighbors.…”

Section: Feature Extractionmentioning

confidence: 99%

Combining deep features and hand-crafted features for abnormality detection in WCE images

Amiri

Hassanpour

Beghdadi³

2023

Multimed Tools Appl

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In this paper, a computer-aided method is proposed for abnormality detection Wireless Capsule Endoscopy (WCE) video frames. Common abnormalities in WCE images include ulcers, bleeding, Angiodysplasia, Lymphoid Hyperplasia, and polyp. In this paper, deep features and Hand-crafted features are combined to detect these abnormalities in WCE images. There are no sufficient images to train deep structures therefore the ResNet50 pertained model is used to extract deep features. Hand-crafted features are associated with color, shape, and texture. They are extracted from the region of interest (ROI), i.e. suspicious region. The expectation Maximization (EM) algorithm is used to extract more distinct areas in the background as ROI. The expectation Maximization (EM) algorithm is configured in a way that can extract the area with a distinct texture and color as ROI. The EM algorithm is also initialized with a new fast method which leads to an increase in the accuracy of the method. We also used a novel idea to reveal unexpected color changes in the background due to existing lesions as a feature set. A large number of features are created by the method, so the minimum redundancy maximum relevance approach is used to select a subset of more effective features. These selected features are then fed to a Support Vector Machine for classification. The results show that the proposed approach can detect mentioned abnormalities in WCE frames with the accuracy of 97.82%

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Section: Feature Extractionmentioning

confidence: 99%

Combining deep features and hand-crafted features for abnormality detection in WCE images

Amiri

Hassanpour

Beghdadi³

2023

Multimed Tools Appl

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“…Pattern recognition and classification methods are applied to a vast range of real-world applications such as image classification [1][2][3], disease detection [4], text classification [5], and so on. The significant growth in these applications shows the necessity of fast and classifiers.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Twin Support Vector Machine for Solving Unclassiﬁable Region Problem

Shakibian

Nasiri

2022

IJE

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Support Vector Machine classifiers are widely used in many classification tasks. However, they have two considerable weaknesses, Unclassifiable Region (UR) in multiclass classification and outliers. In this research, we address these problems by introducing Probabilistic Least Square Twin Support Vector Machine (PLS-TSVM). The proposed algorithm introduces continuous and probabilistic outputs over the model obtained by Least-Square Twin Support Vector Machine (LS-TSVM) method with both linear and polynomial kernel functions. PLS-TSVM not only solves the unclassifiable region problem by introducing a continuous output value membership function, but it also reduces the adverse effects of noisy data and outliers. For showing the superiority of our proposed method, we have conducted experiments on various UCI datasets. In the most cases, higher or competitive accuracy to other methods have been obtained such that in some UCI datasets, PLS-TSVM could obtain up to 99.90% of classification accuracy. Moreover, PLS-TSVM has been evaluated against "one-against-all" and "oneagainst-one" approaches on several well-known video datasets such as Weizmann, KTH, and UCF101 for human action recognition task. The results show the higher accuracy of PLS-TSVM compared to its counterparts. Specifically, the proposed algorithm could improve respectively about 12.2%, 2.8%, and 12.1% of classification accuracy in three video datasets compared to the standard SVM and LS-TSVM classifiers. The final results indicate that the proposed algorithm could achieve better overall performances than the literature.

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Content-based Image Retrieval Considering Colour Difference Histogram of Image Texture and Edge Orientation

Cited by 2 publications

References 33 publications

Combining deep features and hand-crafted features for abnormality detection in WCE images

Combining deep features and hand-crafted features for abnormality detection in WCE images

Probabilistic Twin Support Vector Machine for Solving Unclassiﬁable Region Problem

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