{"doi":"10.1109/cisp-bmei.2018.8633249","title":"Research on Clothing Image Classification by Convolutional Neural Networks","abstract":null,"journal":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","year":2018,"id":684097,"datarank":0.26876392038420827,"base_score":1.791759469228055,"endowment":1.791759469228055,"self_citation_contribution":0.26876392038420827,"citation_network_contribution":0.0,"self_endowment_contribution":0.26876392038420827,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"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":1787174,"name":"Runping Han","orcid":null,"position":1,"is_corresponding":false},{"id":1787175,"name":"Shaopeng Xing","orcid":null,"position":2,"is_corresponding":false},{"id":1787176,"name":"Shuiqiang Ru","orcid":null,"position":3,"is_corresponding":false},{"id":385807,"name":"Lili Chen","orcid":"0000-0001-6726-9782","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Research on Clothing Image Classification by Convolutional Neural Networks","abstract":"In recent years, clothing image classification methods based on convolutional neural networks (CNNs) have attracted plenty of attention with the increasing demand for high accuracy clothing image classification in many fields. In this paper, five different CNNs are designed to implement clothing image classification, which are the conventional CNN, the CNN containing inception module, the CNN containing inception module and residual block, two transfer learned CNNs. The experimental results show that all the networks are capable of achieving good classification, among which the transfer learned CNN have higher classification accuracy.","is_dataset_classified":null,"base_score":1.791759469228055,"endowment":1.791759469228055,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":null,"pmcid":null,"openalex_id":"https://openalex.org/W2911497689","authors":[],"funders":[],"total_grants":0,"fwci":0.1521,"citation_percentile":0.54965469,"influential_citations":0,"citation_trend":[{"year":2019,"count":1},{"year":2020,"count":1},{"year":2022,"count":2},{"year":2024,"count":1}],"oa_status":"closed","license":null,"oa_locations":[{"url":"http://xplorestaging.ieee.org/ielx7/8620379/8633036/08633249.pdf?arnumber=8633249","host_type":"publisher"},{"url":"https://doi.org/10.1109/cisp-bmei.2018.8633249","host_type":""}],"fields_of_study":["Generative Adversarial Networks and Image Synthesis","Industrial Vision Systems and Defect Detection","Advanced Image Processing Techniques"],"mesh_terms":[],"keywords":["Convolutional neural network","Clothing","Contextual image classification","Computer science","Artificial intelligence","Pattern recognition (psychology)","Block (permutation group theory)","Transfer of learning","Image (mathematics)","Computer vision","Mathematics"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-18T13:17:57.127812Z","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":[]}