Anzeige
Erschienen in:
2011 | OriginalPaper | Buchkapitel
Integrating Local Features into Discriminative Graphlets for Scene Classification
verfasst von : Luming Zhang, Wei Bian, Mingli Song, Dacheng Tao, Xiao Liu
Erschienen in: Neural Information Processing
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Scene classification plays an important role in multimedia information retrieval. Since local features are robust to image transformation, they have been used extensively for scene classification. However, it is difficult to encode the spatial relations of local features in the classification process. To solve this problem, Geometric Local Features Integration(GLFI) is proposed. By segmenting a scene image into a set of regions, a so-called Region Adjacency Graph(RAG) is constructed to model their spatial relations. To measure the similarity of two RAGs, we select a few discriminative templates and then use them to extract the corresponding discriminative graphlets(connected subgraphs of an RAG). These discriminative graphlets are further integrated by a boosting strategy for scene classification. Experiments on five datasets validate the effectiveness of our GLFI.