İçerik tabanlı görüntü erişimi için anlamsal sahne sınıflandırması

Content-based image indexing and retrieval have become important researchproblems with the use of large databases in a wide range of areas. Because of theconstantly increasing complexity of the image content, low-level features are nolonger sufficient for image content representation. In this study, a content-basedimage retrieval framework that is based on scene classification for image indexingis proposed. First, the images are segmented into regions by using their color andline structure information. By using the line structures of the images the regionsthat do not consist of uniform colors such as man made structures are captured.After all regions are clustered, each image is represented with the histogram ofthe region types it contains. Both multi-class and one-class classification modelsare used with these histograms to obtain the probability of observing differentsemantic classes in each image. Since a single class with the highest probabilityis not sufficient to model image content in an unconstrained data set with a largenumber of semantically overlapping classes, the obtained probability values areused as a new representation of the images and retrieval is performed on thesenew representations. In order to minimize the semantic gap, a relevance feedbackapproach that is based on the support vector data description is also incorporated.Experiments are performed on both Corel and TRECVID datasets and successfulresults are obtained.