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Image sequence sorting algorithm for commercial tasks
dc.contributor.author | Grelier, Guillaume | |
dc.contributor.author | Casal, Miguel A. | |
dc.contributor.author | Torrente-Patiño, Álvaro | |
dc.contributor.author | Romero, Juan | |
dc.date.accessioned | 2024-05-28T16:33:39Z | |
dc.date.available | 2024-05-28T16:33:39Z | |
dc.date.issued | 2024-04 | |
dc.identifier.citation | Grelier, G., Casal, M. A., Torrente-Patiño, A., & Romero, J. (2024). Image sequence sorting algorithm for commercial tasks. Frontiers in Artificial Intelligence, 7, 1382566. https://doi.org/10.3389/frai.2024.1382566 | es_ES |
dc.identifier.issn | 2624-8212 | |
dc.identifier.uri | http://hdl.handle.net/2183/36677 | |
dc.description.abstract | [Abstract]: Introduction: The sorting of sequences of images is crucial for augmenting user engagement in various virtual commercial platforms, particularly within the real estate sector. A coherent sequence of images respecting room type categorization significantly enhances the intuitiveness and seamless navigation of potential customers through listings. Methods: This study methodically formalizes the challenge of image sequence sorting and expands its applicability by framing it as an ordering problem. The complexity lies in devising a universally applicable solution due to computational demands and impracticality of exhaustive searches for optimal sequencing. To tackle this, our proposed algorithm employs a shortest path methodology grounded in semantic similarity between images. Tailored specifically for the real estate sector, it evaluates diverse similarity metrics to efficiently arrange images. Additionally, we introduce a genetic algorithm to optimize the selection of semantic features considered by the algorithm, further enhancing its effectiveness. Results: Empirical evidence from our dataset demonstrates the efficacy of the proposed methodology. It successfully organizes images in an optimal sequence across 85% of the listings, showcasing its effectiveness in enhancing user experience in virtual commercial platforms, particularly in real estate. Conclusion: This study presents a novel approach to sorting sequences of images in virtual commercial platforms, particularly beneficial for the real estate sector. The proposed algorithm effectively enhances user engagement by providing more intuitive and visually coherent image arrangements. | es_ES |
dc.description.sponsorship | The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study has been funded by the Consolidation Aid of the Xunta ED431C 2022/46. Also, this work received funding with reference PID2020-118362RB-I00, from the State Program of R+D+i Oriented to the Challenges of the Society of the Spanish Ministry of Science, Innovation, and Universities. Finally, PhotoILike received support from the Spanish Ministry for Science and Technology, through the Center for The Industrial Technological Development (CDTI), with the 2023 NEOTEC Grant (SNEO-20222114). | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2022/46 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Frontiers Media | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-118362RB-I00/ES/PREDICCION DE LA PERCEPCION ESTETICA HUMANA MEDIANTE INTELIGENCIA ARTIFICIAL/ | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/SNEO-20222114/ES/ | es_ES |
dc.relation.uri | https://doi.org/10.3389/frai.2024.1382566 | es_ES |
dc.rights | Atribución 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Image ordering | es_ES |
dc.subject | Semantic representation | es_ES |
dc.subject | Image embedding | es_ES |
dc.subject | Real estate | es_ES |
dc.subject | Evolutionary computing | es_ES |
dc.subject | Feature selection | es_ES |
dc.title | Image sequence sorting algorithm for commercial tasks | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Frontiers in Artificial Intelligence | es_ES |
UDC.volume | 7 | es_ES |
UDC.startPage | 1 | es_ES |
UDC.endPage | 10 | es_ES |
dc.identifier.doi | 10.3389/frai.2024.1382566 |
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