{"doi":"10.1109/i2ct.2018.8529815","title":"Comparison of Pixel N-Grams with Histogram, Haralick's features and Bag-of-Visual-Words for Texture Image Classification","abstract":null,"journal":"2018 3rd International Conference for Convergence in Technology (I2CT)","year":2018,"id":633538,"datarank":0.16479184330021646,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.0,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1621247,"name":"Andrew Stranieri","orcid":null,"position":1,"is_corresponding":false},{"id":1642676,"name":"Pradnya Kulkarni","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Comparison of Pixel N-Grams with Histogram, Haralick's features and Bag-of-Visual-Words for Texture Image Classification","abstract":"Texture image classification is very useful in many domains. It has been tried using statistical., spectral and structural approaches. A novel Pixel N-grams technique has emerged for image feature extraction recently. The aim of this paper is to analyse the efficacy of Pixel N-grams technique for texture image classification in comparison with the traditional techniques namely Intensity histogram., Haralick's features based on co-occurrence matrix and state-of-the-art Bag-of-Visual-Words (BoVW). The experiments were carried out on the benchmark UIUC texture dataset using SVM classifier. The classification performance was compared using Fscore., Recall and Precision. The classification results using Pixel N-gram were significantly better than that using Intensity histogram and Haralick features whereas., they were comparable with the BoVwapproach.","is_dataset_classified":null,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19767382","pmcid":null,"openalex_id":"https://openalex.org/W2901148276","authors":[],"funders":[],"total_grants":0,"fwci":0.0761,"citation_percentile":0.39937955,"influential_citations":0,"citation_trend":[{"year":2019,"count":1},{"year":2024,"count":1}],"oa_status":"closed","license":"https://doi.org/10.15223/policy-029","oa_locations":[{"url":"http://xplorestaging.ieee.org/ielx7/8509796/8529154/08529815.pdf?arnumber=8529815","host_type":"publisher"},{"url":"https://doi.org/10.1109/i2ct.2018.8529815","host_type":""},{"url":"http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/180880","host_type":"repository"}],"fields_of_study":["Image Retrieval and Classification Techniques","Advanced Image and Video Retrieval Techniques","Remote-Sensing Image Classification"],"mesh_terms":[],"keywords":["Artificial intelligence","Histogram","Pattern recognition (psychology)","Pixel","Computer science","Support vector machine","Bag-of-words model in computer vision","Feature extraction","Image texture","Contextual image classification","Classifier (UML)","Texture (cosmology)","Computer vision","Co-occurrence matrix","Visual Word","Image (mathematics)","Image retrieval","Image segmentation"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-06T12:14:43.210443Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}