Semantic Guided Interactive Image Retrieval for plant identification

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dc.contributor.authorGonçalves, Filipe Marcel Fernandes [UNESP]
dc.contributor.authorGuilherme, Ivan Rizzo [UNESP]
dc.contributor.authorPedronette, Daniel Carlos Guimarães [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2018-12-11T17:14:24Z
dc.date.available2018-12-11T17:14:24Z
dc.date.issued2018-01-01
dc.description.abstractA lot of images are currently generated in many domains, requiring specialized knowledge of identification and analysis. From one standpoint, many advances have been accomplished in the development of image retrieval techniques based on visual image properties. However, the semantic gap between low-level features and high-level concepts still represents a challenging scenario. On another standpoint, knowledge has also been structured in many fields by ontologies. A promising solution for bridging the semantic gap consists in combining the information from low-level features with semantic knowledge. This work proposes a novel graph-based approach denominated Semantic Interactive Image Retrieval (SIIR) capable of combining Content Based Image Retrieval (CBIR), unsupervised learning, ontology techniques and interactive retrieval. To the best of our knowledge, there is no approach in the literature that combines those diverse techniques like SIIR. The proposed approach supports expert identification tasks, such as the biologist's role in plant identification of Angiosperm families. Since the system exploits information from different sources as visual content, ontology, and user interactions, the user efforts required are drastically reduced. For the semantic model, we developed a domain ontology which represents the plant properties and structures, relating features from Angiosperm families. A novel graph-based approach is proposed for combining the semantic information and the visual retrieval results. A bipartite and a discriminative attribute graph allow a semantic selection of the most discriminative attributes for plant identification tasks. The selected attributes are used for formulating a question to the user. The system updates similarity information among images based on the user's answer, thus improving the retrieval effectiveness and reducing the user's efforts required for identification tasks. The proposed method was evaluated on the popular Oxford Flowers 17 and 102 Classes datasets, yielding highly effective results in both datasets when compared to other approaches. For example, the first five retrieved images for 17 classes achieve a retrieval precision of 97.07% and for 102 classes, 91.33%.en
dc.description.affiliationDepartment of Statistics Applied Mathematics and Computing (DEMAC) State University of São Paulo (UNESP), Av. 24-A, 1515
dc.description.affiliationUnespDepartment of Statistics Applied Mathematics and Computing (DEMAC) State University of São Paulo (UNESP), Av. 24-A, 1515
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipIdFAPESP: 2013/08645-0
dc.format.extent12-26
dc.identifierhttp://dx.doi.org/10.1016/j.eswa.2017.08.035
dc.identifier.citationExpert Systems with Applications, v. 91, p. 12-26.
dc.identifier.doi10.1016/j.eswa.2017.08.035
dc.identifier.file2-s2.0-85028510372.pdf
dc.identifier.issn0957-4174
dc.identifier.scopus2-s2.0-85028510372
dc.identifier.urihttp://hdl.handle.net/11449/175105
dc.language.isoeng
dc.relation.ispartofExpert Systems with Applications
dc.relation.ispartofsjr1,271
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectInteractive image retrieval
dc.subjectOntology
dc.subjectSemantic gap
dc.subjectUnsupervised learning
dc.titleSemantic Guided Interactive Image Retrieval for plant identificationen
dc.typeArtigo

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Artigos - Estatistica, Matemática Aplicada e Computação - IGCE