29 articles for “texture feature extraction”

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Research ArticleOpen accessPhrase in the titleLT-LBP-Based Spatial Texture Feature Extraction with Deep Learning for X-Ray ImagesPankaja Lakshmi P., Sivagami M.The novel logarithmic transformation of Local Binary Pattern (LT-LBP) for texture feature extraction is used with Deep Learning (DL) models. The Computational complexity in the DL models asymptotically increases with the data sesssst and the depth of the layers in Convolutional Neural Networks (CNN). Pre-processing the images before training the DL model holds a strong impact on the operation speed … …Journal of Computer Science, Vol. 20, Issue 1 · 18 Dec 2023 · 7,190 accessesView PDF Cite

Research ArticleOpen accessPhrase in the titleMultipatch-GLCM for Texture Feature Extraction on Classification of the Colon Histopathology Images using Deep Neural Network with GPU AccelerationToto Haryanto, Adib Pratama, Heru Suhartanto et al.Cancer is one of the leading causes of death in the world. It is the main reason why research in this field becomes challenging. Not only for the pathologist but also from the view of a computer scientist. Hematoxylin and Eosin (H&E) images are the most common modalities used by the pathologist for cancer detection. The status of cancer with … …Journal of Computer Science, Vol. 16, Issue 3 · 5 Mar 2020 · 9,215 accessesView PDF Cite

Research ArticleOpen accessPhrase in the titleWAVELET BASED CONTENT BASED IMAGE RETRIEVAL USING COLOR AND TEXTURE FEATURE EXTRACTION BY GRAY LEVEL COOCURENCE MATRIX AND COLOR COOCURENCE MATRIXJeyanthi Prabhu, Jawahar Senthil KumarIn this study we proposes an effective content based image retrieval by color and texture based on wavelet coefficient method to achieve good retrieval in efficiency. Color feature extraction is done by color Histogram. The texture feature extraction is acquired by Gray Level Coocurence Matrix (GLCM) or Color Coocurence Matrix (CCM). This study provides better result for image retrieval by … …Journal of Computer Science, Vol. 10, Issue 1 · 7 Nov 2013 · 7,627 accessesView PDF Cite

Research ArticleOpen accessIn the keywordsCONTENT BASED BATIK IMAGE RETRIEVALA. Haris Rangkuti, Agus Harjoko, Agfianto Eko PutroContent Based Batik Image Retrieval (CBBIR) is an area of research that focuses on image processing based on characteristic motifs of batik. Basically the image has a unique batik motif compared with other images. Its uniqueness lies in the characteristics possessed texture and shape, which has a unique and distinct characteristics compared with other image characteristics. To study this batik … …Journal of Computer Science, Vol. 10, Issue 6 · 27 Jan 2014 · 7,417 accessesView PDF Cite

Research ArticleOpen accessIn the keywordsContent Based Medical Image Retrieval with Texture Content Using Gray Level Co-occurrence Matrix and K-Means Clustering AlgorithmsB. Ramamurthy, K. R. ChandranProblem statement: Recently, there has been a huge progress in collection of varied image databases in the form of digital. Most of the users found it difficult to search and retrieve required images in large collections. In order to provide an effective and efficient search engine tool, the system has been implemented. In image retrieval system, there is no methodologies … …Journal of Computer Science, Vol. 8, Issue 7 · 24 May 2012 · 7,448 accessesView PDF Cite

Research ArticleOpen accessIn the abstractEpsilon-Insensitive Primal Loss With Support Vector Machine for Robust Cotton Leaf Disease ClassificationPavana Kumar Eramachetty Dhanajayalu, Malathi Sivasamy, Chitveli Siva Balaji YadavCotton diseases significantly reduce both yield and quality of crops, making early and accurate identification of the disease crucial for effective prevention and control. However, achieving a balance between lightweight models and precise classification of cotton diseases remains challenging. This difficulty arises primarily from the dispersed nature of disease symptoms and interference from noisy backgrounds, which complicate the detection process. … …Journal of Computer Science, Vol. 22, Issue 6 · 5 Aug 2026 · 755 accessesView PDF Cite

Research ArticleOpen accessIn the abstractHybrid CNN-Based Transformer Pipeline With Radiomic Fusion for Multi-Class Lung Cancer DetectionAanchal Vij, Kuldeep Singh Kaswan, Anand NayyarEarly detection of lung cancer remains challenging due to high intra-class variation and inter-class similarity in Computed Tomography (CT) images. In this paper, we propose a hybrid deep learning model that combines convolutional, attention-based, and transformer-guided representations to address these challenges in multi-class lung cancer classification. For deep feature extraction, we use the EfficientNetV2-S architecture augmented with a Convolutional Block … …Journal of Computer Science, Vol. 22, Issue 6 · 3 Jun 2026 · 1,528 accessesView PDF Cite

Review ArticleOpen accessIn the abstractImproving Classification and Clustering Accuracy of Palembang Duku Fruit Quality via CNN-K-Means IntegrationHenny Hartono, Francka Sakti Lee, Herlina Herlina et al.Duku Palembang (Lansium domesticum Corr.) is a leading commodity from South Sumatra with high economic value. The quality assessment process for duku has traditionally relied on manual visual inspection, which is subjective, time-consuming, and inconsistent, especially at large production scales. This study proposes an integrative framework combining Convolutional Neural Network (CNN) and K-Means Clustering to improve the accuracy of classification … …OnLine Journal of Biological Sciences, Vol. 26, Issue 2 · 8 May 2026 · 1,475 accessesView PDF Cite

Research ArticleOpen accessIn the abstractAnomaly Detection Based on Vision Transformer Model and Texture FeaturesMohammed Lahraichi, Abdelhafid Berroukham, Khalid HousniAnomaly detection is one of the video surveillance applications,which aims to detect and analyze abnormal behaviors and risky situations inorder to prevent accidents. Various deep learning models have beenpreviously developed for this purpose, such as CNN, RNN, and VisionTransformer, each one has its strengths and weaknesses based on the qualityof input data. This paper proposes a novel approach based on … …Journal of Computer Science, Vol. 21, Issue 7 · 10 Jul 2025 · 3,682 accessesView PDF Cite

Research ArticleOpen accessIn the abstractDetection of Rotten Fresh-Cut Cauliflowers based on Machine Vision Technology and Watershed Segmentation MethodJianxin Xue, Liang Huang, Bingyu Mu et al.In this study, machine vision technology was used to separate the samples and detect the rotting degrees of fresh-cut cauliflowers. First, the improved watershed algorithm was used for the segmentation of fresh-cut cauliflower samples and the extraction of single-sample. Then, three color models, a gray co-occurrence matrix and two feature extraction algorithms were used to extract the color, texture and … …American Journal of Biochemistry and Biotechnology, Vol. 18, Issue 2 · 13 Apr 2022 · 6,083 accessesView PDF Cite