{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T12:39:08Z","timestamp":1755693548397,"version":"3.41.2"},"reference-count":32,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T00:00:00Z","timestamp":1683244800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["DTA"],"published-print":{"date-parts":[[2023,10,20]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>The problem of image retrieval and image description exists in various fields. In this paper, a model of content-based image retrieval and image content extraction based on the KD-Tree structure was proposed.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>A Random Forest structure was built to classify the objects on each image on the basis of the balanced multibranch KD-Tree structure. From that purpose, a KD-Tree structure was generated by the Random Forest to retrieve a set of similar images for an input image. A KD-Tree structure is applied to determine a relationship word at leaves to extract the relationship between objects on an input image. An input image content is described based on class names and relationships between objects.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>A model of image retrieval and image content extraction was proposed based on the proposed theoretical basis; simultaneously, the experiment was built on multi-object image datasets including Microsoft COCO and Flickr with an average image retrieval precision of 0.9028 and 0.9163, respectively. The experimental results were compared with those of other works on the same image dataset to demonstrate the effectiveness of the proposed method.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>A balanced multibranch KD-Tree structure was built to apply to relationship classification on the basis of the original KD-Tree structure. Then, KD-Tree Random Forest was built to improve the classifier performance and retrieve a set of similar images for an input image. Concurrently, the image content was described in the process of combining class names and relationships between objects.<\/jats:p><\/jats:sec>","DOI":"10.1108\/dta-06-2022-0247","type":"journal-article","created":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T13:02:17Z","timestamp":1683291737000},"page":"514-536","source":"Crossref","is-referenced-by-count":4,"title":["A model of image retrieval based on KD-Tree Random Forest"],"prefix":"10.1108","volume":"57","author":[{"given":"Nguyen Thi","family":"Dinh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nguyen Thi Uyen","family":"Nhi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thanh Manh","family":"Le","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8408-2004","authenticated-orcid":false,"given":"Thanh The","family":"Van","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2023,5,5]]},"reference":[{"issue":"2","key":"key2023102013125222200_ref001","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1080\/10095020.2017.1399674","article-title":"Object-based classification of hyperspectral data using random forest algorithm","volume":"21","year":"2018","journal-title":"Geo-spatial Information Science"},{"issue":"9","key":"key2023102013125222200_ref002","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1145\/361002.361007","article-title":"Multidimensional binary search trees used for associative searching","volume":"18","year":"1975","journal-title":"Communications of the ACM"},{"key":"key2023102013125222200_ref003","doi-asserted-by":"publisher","first-page":"107164","DOI":"10.1016\/j.patcog.2019.107164","article-title":"Semi-supervised robust deep neural networks for multi-label image classification","volume":"100","year":"2020","journal-title":"Pattern Recognition"},{"year":"2017","author":"COCO","key":"key2023102013125222200_ref004"},{"first-page":"177","article-title":"An improvement method of KD-Tree using k-means and k-NN for semantic-based image retrieval system","year":"2022","key":"key2023102013125222200_ref005"},{"key":"key2023102013125222200_ref006","first-page":"33","article-title":"Image classification using KD-Tree for image retrieval problem","volume":"1","year":"2021","journal-title":"Journal of Research and Development on Information and Communication Technology"},{"key":"key2023102013125222200_ref007","doi-asserted-by":"publisher","first-page":"327","DOI":"10.15625\/vap.2021.0075","article-title":"A method of image classification base on KD-tree structure for semantic-based image retrieval system","year":"2021"},{"first-page":"278","article-title":"Analysis of Flickr images using feature extraction techniques","year":"2019","key":"key2023102013125222200_ref008"},{"year":"2017","author":"Flickr","key":"key2023102013125222200_ref009"},{"key":"key2023102013125222200_ref010","doi-asserted-by":"publisher","first-page":"902","DOI":"10.1109\/IICSPI.2018.8690508","article-title":"An advanced k nearest neighbor classification algorithm based on KD-tree","year":"2018"},{"key":"key2023102013125222200_ref011","doi-asserted-by":"publisher","first-page":"1584","DOI":"10.1145\/3123266.3123403","article-title":"Pseudo label based unsupervised deep discriminative hashing for image retrieval","year":"2017"},{"issue":"28","key":"key2023102013125222200_ref012","first-page":"35217","article-title":"M-ary random forest \u2013 A new multidimensional partitioning approach to random forest","volume":"80","year":"2021","journal-title":"Multimedia Tools and Applications"},{"issue":"4","key":"key2023102013125222200_ref013","doi-asserted-by":"crossref","first-page":"1820","DOI":"10.1109\/TIP.2017.2666038","article-title":"Nonlinear deep kernel learning for image annotation","volume":"26","year":"2017","journal-title":"IEEE Transactions on Image Processing"},{"issue":"3","key":"key2023102013125222200_ref014","first-page":"19","article-title":"Comparison of bagging and voting ensemble machine learning algorithm as a classifier","volume":"9","year":"2019","journal-title":"International Journals of Advanced Research in Computer Science and Software Engineering"},{"issue":"7","key":"key2023102013125222200_ref015","doi-asserted-by":"crossref","first-page":"1956","DOI":"10.1007\/s11263-020-01316-z","article-title":"The open images dataset v4","volume":"128","year":"2020","journal-title":"International Journal of Computer Vision"},{"first-page":"16478","article-title":"General multi-label image classification with transformers","year":"2021","key":"key2023102013125222200_ref016"},{"key":"key2023102013125222200_ref017","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.eswa.2019.07.006","article-title":"Learning deep neural networks for node classification","volume":"137","year":"2019","journal-title":"Expert Systems with Applications"},{"first-page":"4654","article-title":"Visual semantic reasoning for image-text matching","year":"2019","key":"key2023102013125222200_ref018"},{"first-page":"375","article-title":"Knowing when to look: adaptive attention via a visual sentinel for image captioning","year":"2017","key":"key2023102013125222200_ref019"},{"first-page":"346","article-title":"Image classification based on improved random forest algorithm","year":"2018","key":"key2023102013125222200_ref020"},{"first-page":"178","article-title":"Structured query-based image retrieval using scene graphs","year":"2020","key":"key2023102013125222200_ref021"},{"first-page":"32","article-title":"Binary generative adversarial networks for image retrieval","year":"2018","key":"key2023102013125222200_ref022"},{"issue":"11","key":"key2023102013125222200_ref023","doi-asserted-by":"publisher","first-page":"1086","DOI":"10.1080\/2150704X.2019.1649736","article-title":"Hyperspectral image classification based on convolutional neural network and random forest","volume":"10","year":"2019","journal-title":"Remote Sensing Letters"},{"first-page":"2285","article-title":"Cnn-rnn: a unified framework for multi-label image classification","year":"2016","key":"key2023102013125222200_ref024"},{"first-page":"5764","article-title":"Camp: cross-modal adaptive message passing for text-image retrieval","year":"2019","key":"key2023102013125222200_ref025"},{"issue":"11","key":"key2023102013125222200_ref026","first-page":"7250","article-title":"Multilabel image classification via feature\/label co-projection","volume":"51","year":"2020","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"key2023102013125222200_ref027","doi-asserted-by":"publisher","first-page":"105661","DOI":"10.1016\/j.knosys.2020.105661","article-title":"A similarity-based two-view multiple instance learning method for classification","volume":"201","year":"2020","journal-title":"Knowledge-Based Systems"},{"first-page":"10718","article-title":"Image-to-image retrieval by learning similarity between scene graphs","year":"2021","key":"key2023102013125222200_ref028"},{"key":"key2023102013125222200_ref029","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1109\/MLBDBI48998.2019.00046","article-title":"KD-tree based efficient ensemble classification algorithm for imbalanced learning","year":"2019"},{"first-page":"3536","article-title":"Context-aware attention network for image-text retrieval","year":"2020","key":"key2023102013125222200_ref030"},{"first-page":"64","article-title":"Fast face sketch synthesis via kd-tree search","year":"2016","key":"key2023102013125222200_ref031"},{"key":"key2023102013125222200_ref032","doi-asserted-by":"publisher","first-page":"107100","DOI":"10.1016\/j.patcog.2019.107100","article-title":"Large-scale multi-label classification using unknown streaming images","volume":"99","year":"2020","journal-title":"Pattern Recognition"}],"container-title":["Data Technologies and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/DTA-06-2022-0247\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/DTA-06-2022-0247\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T23:15:12Z","timestamp":1753398912000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/dta\/article\/57\/4\/514-536\/25263"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,5]]},"references-count":32,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,5,5]]},"published-print":{"date-parts":[[2023,10,20]]}},"alternative-id":["10.1108\/DTA-06-2022-0247"],"URL":"https:\/\/doi.org\/10.1108\/dta-06-2022-0247","relation":{},"ISSN":["2514-9288","2514-9288"],"issn-type":[{"type":"print","value":"2514-9288"},{"type":"electronic","value":"2514-9288"}],"subject":[],"published":{"date-parts":[[2023,5,5]]}}}