{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T06:26:07Z","timestamp":1763533567350,"version":"3.45.0"},"reference-count":51,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T00:00:00Z","timestamp":1750723200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The main challenge in content-based image retrieval (CBIR) systems is accurately describing image features in a manner that aligns with human perception. Natural images consist of varying luminance levels and complex, intertwined texture patterns. Decomposing an image into its constituent components can simplify feature extraction and enhance system performance. This paper proposes a method for CBIR based on luminance and texture decomposition. In the luminance component, texture features are suppressed, allowing luminance features to be extracted using AlexNet. Conversely, in the texture component, texture features are amplified and extracted using SqueezeNet. AlexNet captures the global context of images by utilizing spatial information, thereby enhancing feature contrast for better discrimination between object classes. SqueezeNet is selected for its ability to produce compact, highly discriminative feature vectors that effectively describe texture features. To mitigate redundancy caused by feature overlap, the most relevant features are selected using the Boruta\u2013Shap algorithm. The feature space is visualized using the t-distributed stochastic neighbor embedding (t-SNE) technique, and the interpretability of the proposed approach is evaluated through Shapley value analysis. Experimental results demonstrate the effectiveness of the proposed approach in improving CBIR performance.<\/jats:p>","DOI":"10.1093\/comjnl\/bxaf067","type":"journal-article","created":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T07:52:04Z","timestamp":1749196324000},"page":"1699-1710","source":"Crossref","is-referenced-by-count":0,"title":["Content-based image retrieval based on luminance and texture decomposition"],"prefix":"10.1093","volume":"68","author":[{"given":"Fatemeh","family":"Taheri","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, ST.C., Islamic Azad University"}]},{"given":"Kambiz","family":"Rahbar","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, ST.C., Islamic Azad University"}]}],"member":"286","published-online":{"date-parts":[[2025,6,24]]},"reference":[{"key":"2025111901221192200_ref1","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1177\/0165551518782825","article-title":"A novel method for content-based image retrieval to improve the effectiveness of the bag-of-words model using a support vector machine","volume":"45","author":"Sarwar","year":"2018","journal-title":"J Inf Sci"},{"key":"2025111901221192200_ref2","first-page":"178","volume-title":"Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, Sante Fe, NM, USA, 2002-January","author":"Park","year":"2002"},{"key":"2025111901221192200_ref3","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant Keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int J Comput Vis"},{"key":"2025111901221192200_ref4","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/J.CVIU.2007.09.014","article-title":"Speeded-up robust features (SURF)","volume":"110","author":"Bay","year":"2008","journal-title":"Comput Vis Image Underst"},{"key":"2025111901221192200_ref5","first-page":"3384","volume-title":"Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition","author":"Perronnin","year":"2010"},{"key":"2025111901221192200_ref6","first-page":"1578","volume-title":"Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition","author":"Arandjelovic","year":"2013"},{"key":"2025111901221192200_ref7","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.1016\/J.NEUCOM.2015.08.076","article-title":"Fine-residual VLAD for image retrieval","volume":"173","author":"Liu","year":"2016","journal-title":"Neurocomputing"},{"key":"2025111901221192200_ref8","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/J.NEUCOM.2015.10.044","article-title":"A hierarchal BoW for image retrieval by enhancing feature salience","volume":"175","author":"Jiang","year":"2016","journal-title":"Neurocomputing"},{"key":"2025111901221192200_ref9","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun ACM"},{"volume-title":"Computer Vision \u2013 ECCV 2014. ECCV 2014","author":"Babenko","key":"2025111901221192200_ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10590-1_38"},{"key":"2025111901221192200_ref11","doi-asserted-by":"publisher","first-page":"1655","DOI":"10.1109\/TPAMI.2018.2846566","article-title":"Fine-tuning CNN image retrieval with no human annotation","volume":"41","author":"Radenovic","year":"2019","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2025111901221192200_ref12","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.1109\/TPAMI.2017.2711011","article-title":"NetVLAD: CNN architecture for weakly supervised place recognition","volume":"40","author":"Arandjelovic","year":"2018","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2025111901221192200_ref13","first-page":"327","volume-title":"ICMR 2016 - Proceedings of the 2016 ACM International Conference on Multimedia Retrieval","author":"Mohedano","year":"2016"},{"key":"2025111901221192200_ref14","doi-asserted-by":"publisher","first-page":"109241","DOI":"10.1016\/J.PATCOG.2022.109241","article-title":"A new image decomposition approach using pixel-wise analysis sparsity model","volume":"136","author":"Du","year":"2023","journal-title":"Pattern Recogn"},{"key":"2025111901221192200_ref15","doi-asserted-by":"publisher","first-page":"25028","DOI":"10.1007\/s10489-023-04875-z","article-title":"Structurally incoherent adaptive weighted low-rank matrix decomposition for image classification","volume":"53","author":"Li","year":"2023","journal-title":"Appl Intell"},{"key":"2025111901221192200_ref16","doi-asserted-by":"publisher","first-page":"8215","DOI":"10.1109\/JSTARS.2023.3296505","article-title":"An adaptive multi-scale Gaussian Co-occurrence filtering decomposition method for multispectral and SAR image fusion","volume":"16","author":"Gong","year":"2023","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"2025111901221192200_ref17","doi-asserted-by":"publisher","first-page":"37959","DOI":"10.1007\/S11042-022-13670-W","article-title":"Effective features in content-based image retrieval from a combination of low-level features and deep Boltzmann machine","volume":"82","author":"Taheri","year":"2022","journal-title":"Multimed Tools Appl"},{"key":"2025111901221192200_ref18","doi-asserted-by":"publisher","first-page":"4009","DOI":"10.1007\/s11760-023-02631-x","article-title":"Innovative local texture descriptor in joint of human-based color features for content-based image retrieval","volume":"17","author":"Kelishadrokhi","year":"2023","journal-title":"SIViP"},{"key":"2025111901221192200_ref19","doi-asserted-by":"publisher","first-page":"117639","DOI":"10.1109\/ACCESS.2020.3003911","article-title":"Image retrieval scheme using quantized bins of color image components and adaptive Tetrolet transform","volume":"8","author":"Varish","year":"2020","journal-title":"IEEE Access"},{"key":"2025111901221192200_ref20","doi-asserted-by":"publisher","first-page":"146284","DOI":"10.1109\/ACCESS.2020.3015285","article-title":"Combination of dominant color descriptor and Hu moments in consistent zone for content based image retrieval","volume":"8","author":"Xie","year":"2020","journal-title":"IEEE Access"},{"key":"2025111901221192200_ref21","doi-asserted-by":"publisher","first-page":"31423","DOI":"10.1007\/s11042-023-14678-6","article-title":"Two-layer content-based image retrieval technique for improving effectiveness","volume":"82","author":"Salih","year":"2023","journal-title":"Multimed Tools Appl"},{"key":"2025111901221192200_ref22","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/J.PATREC.2022.06.017","article-title":"Exploiting deep and hand-crafted features for texture image retrieval using class membership","volume":"160","author":"Yelchuri","year":"2022","journal-title":"Pattern Recogn Lett"},{"key":"2025111901221192200_ref23","doi-asserted-by":"publisher","first-page":"1585","DOI":"10.1007\/s10044-019-00787-2","article-title":"Multi-channel local ternary pattern for content-based image retrieval","volume":"22","author":"Agarwal","year":"2019","journal-title":"Pattern Anal Applic"},{"key":"2025111901221192200_ref24","doi-asserted-by":"publisher","first-page":"29901","DOI":"10.1109\/ACCESS.2020.2972973","article-title":"A new illumination-rotation-invariance texture feature based on quasi-periodic signal analysis","volume":"8","author":"Yang","year":"2020","journal-title":"IEEE Access"},{"key":"2025111901221192200_ref25","doi-asserted-by":"publisher","DOI":"10.1007\/S11227-022-04748-1","article-title":"An effective bi-layer content-based image retrieval technique","volume":"79","author":"Salih","year":"2023","journal-title":"J Supercomput"},{"key":"2025111901221192200_ref26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2023\/6257573","article-title":"Color image retrieval method using low dimensional salient visual feature descriptors for IoT applications","volume":"2023","author":"Varish","year":"2023","journal-title":"Comput Intell Neurosci"},{"key":"2025111901221192200_ref27","doi-asserted-by":"publisher","first-page":"1117","DOI":"10.1007\/S11760-021-02061-7","article-title":"Heterogeneous image retrieval based on structural information","volume":"16","author":"Weng","year":"2021","journal-title":"Signal Image Video Process"},{"key":"2025111901221192200_ref28","doi-asserted-by":"publisher","first-page":"29525","DOI":"10.1007\/s11042-022-12819-x","article-title":"Texture image retrieval using DNST domain local neighborhood intensity pattern","volume":"81","author":"Wang","year":"2022","journal-title":"Multimed Tools Appl"},{"key":"2025111901221192200_ref29","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1007\/s10044-015-0509-8","article-title":"On size invariance texture image retrieval by fuzzy logic classifier and scattering statistical features","volume":"19","author":"Wang","year":"2016","journal-title":"Pattern Anal Applic"},{"key":"2025111901221192200_ref30","doi-asserted-by":"publisher","first-page":"1542","DOI":"10.1109\/TIP.2020.3043665","article-title":"Structure-texture image decomposition using discriminative patch recurrence","volume":"30","author":"Xu","year":"2021","journal-title":"IEEE Trans Image Process"},{"key":"2025111901221192200_ref31","doi-asserted-by":"publisher","first-page":"1597","DOI":"10.1007\/s00500-021-06660-x","article-title":"Efficient content-based image retrieval using deep search and rescue algorithm","volume":"26","author":"Keisham","year":"2022","journal-title":"Soft Comput"},{"key":"2025111901221192200_ref32","doi-asserted-by":"publisher","first-page":"101039","DOI":"10.1016\/J.JESTCH.2021.07.002","article-title":"An efficient approach for no-reference image quality assessment based on statistical texture and structural features","volume":"30","author":"Rajevenceltha","year":"2022","journal-title":"Eng Sci Technol"},{"key":"2025111901221192200_ref33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/S13735-023-00292-7","article-title":"Content-based image retrieval using handcraft feature fusion in semantic pyramid","volume":"12","author":"Taheri","year":"2023","journal-title":"Int J Multimed Inf Retr"},{"key":"2025111901221192200_ref34","doi-asserted-by":"publisher","DOI":"10.1007\/S40747-022-00866-8","article-title":"Deep learned vectors\u2019 formation using auto-correlation, scaling, and derivations with CNN for complex and huge image retrieval","volume":"9","author":"Naeem","journal-title":"Complex Intell Syst"},{"key":"2025111901221192200_ref35","doi-asserted-by":"publisher","DOI":"10.1007\/S13042-022-01645-0","article-title":"Exploiting deep textures for image retrieval","volume":"14","author":"Liu","journal-title":"Int J Mach Learn Cybern"},{"key":"2025111901221192200_ref36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/IJCVIP.2020010101","article-title":"Multimedia image retrieval system by combining CNN with handcraft features in three different similarity measures","volume":"10","author":"Alrahhal","year":"2020","journal-title":"Int J Comput Vis Image Process"},{"key":"2025111901221192200_ref37","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1016\/J.PROCS.2023.01.016","article-title":"Insect classification framework based on a novel fusion of high-level and shallow features","volume":"218","author":"Haarika","year":"2023","journal-title":"Procedia Comput Sci"},{"key":"2025111901221192200_ref38","doi-asserted-by":"publisher","first-page":"41934","DOI":"10.1109\/ACCESS.2021.3063545","article-title":"Maximum response deep learning using Markov, retinal primitive patch binding with GoogLeNet VGG-19 for large image retrieval","volume":"9","author":"Ahmed","year":"2021","journal-title":"IEEE Access"},{"key":"2025111901221192200_ref39","doi-asserted-by":"publisher","first-page":"950","DOI":"10.1016\/J.COMPELECENG.2018.01.036","article-title":"Convolutional neural network simplification via feature map pruning","volume":"70","author":"Zou","year":"2018","journal-title":"Comput Electr Eng"},{"article-title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and &lt;1MB model size","year":"2016","author":"Iandola","key":"2025111901221192200_ref40"},{"key":"2025111901221192200_ref41","doi-asserted-by":"publisher","first-page":"77661","DOI":"10.1109\/ACCESS.2021.3079337","article-title":"SqueezeNet and fusion network-based accurate fast fully convolutional network for hand detection and gesture recognition","volume":"9","author":"Qiang","year":"2021","journal-title":"IEEE Access"},{"key":"2025111901221192200_ref42","doi-asserted-by":"publisher","first-page":"2225","DOI":"10.1007\/s11063-022-11079-y","article-title":"Efficient deep feature based semantic image retrieval","volume":"55","author":"Kumar","year":"2023","journal-title":"Neural Process Lett"},{"key":"2025111901221192200_ref43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/JSS.V036.I11","article-title":"Feature selection with the boruta package","volume":"36","author":"Kursa","year":"2010","journal-title":"J Stat Softw"},{"key":"2025111901221192200_ref44","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat Mach Intell"},{"key":"2025111901221192200_ref45","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1016\/J.EJOR.2024.06.023","article-title":"The many Shapley values for explainable artificial intelligence: a sensitivity analysis perspective","volume":"318","author":"Borgonovo","year":"2024","journal-title":"Eur J Oper Res"},{"key":"2025111901221192200_ref46","doi-asserted-by":"publisher","DOI":"10.1007\/S10479-021-04476-4","article-title":"Predicting the next Poga\u010dar: a data analytical approach to detect young professional cycling talents","volume":"325","author":"Janssens","journal-title":"Ann Oper Res"},{"key":"2025111901221192200_ref47","doi-asserted-by":"publisher","first-page":"1075","DOI":"10.1109\/TPAMI.2003.1227984","article-title":"Automatic linguistic indexing of pictures by a statistical modeling approach","volume":"25","author":"Li","year":"2003","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2025111901221192200_ref48","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J Mach Learn Res"},{"key":"2025111901221192200_ref49","article-title":"ImageNet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv Neural Inf Proces Syst"},{"key":"2025111901221192200_ref50","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1109\/CVPR.2016.90","volume-title":"Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2016-December","author":"He","year":"2016"},{"volume-title":"3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings","author":"Simonyan","key":"2025111901221192200_ref51"}],"container-title":["The Computer Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/comjnl\/article-pdf\/68\/11\/1699\/63565805\/bxaf067.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/comjnl\/article-pdf\/68\/11\/1699\/63565805\/bxaf067.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T06:22:22Z","timestamp":1763533342000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/comjnl\/article\/68\/11\/1699\/8172478"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,24]]},"references-count":51,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,6,24]]},"published-print":{"date-parts":[[2025,11,13]]}},"URL":"https:\/\/doi.org\/10.1093\/comjnl\/bxaf067","relation":{},"ISSN":["0010-4620","1460-2067"],"issn-type":[{"type":"print","value":"0010-4620"},{"type":"electronic","value":"1460-2067"}],"subject":[],"published-other":{"date-parts":[[2025,11]]},"published":{"date-parts":[[2025,6,24]]}}}