Browsing by Subject "Scene recognition"
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Item Open Access Conceptfusion: A flexible scene classification framework(Springer, 2015-03-04) Saraç, Mustafa İlker; işcen, Ahmet; Gölge, Eren; Duygulu, PınarWe introduce ConceptFusion, a method that aims high accuracy in categorizing large number of scenes, while keeping the model relatively simpler and efficient for scalability. The proposed method combines the advantages of both low-level representations and high-level semantic categories, and eliminates the distinctions between different levels through the definition of concepts. The proposed framework encodes the perspectives brought through different concepts by considering them in concept groups that are ensembled for the final decision. Experiments carried out on benchmark datasets show the effectiveness of incorporating concepts in different levels with different perspectives. © Springer International Publishing Switzerland 2015.Item Open Access Scene classification with random forests and object and color distributions(IEEE, 2013) İşcen, Ahmet; Gölge, Eren; Armağan, Anıl; Duygulu, PınarWe propose a method to recognize the scene of an image by finding the objects and the colors it contains. We approach this problem by creating a binary vector of detected objects and a histogram of the colors that the image contains. We then use these features to train a random forest classifier in order to determine the scene of each image. For class-based classifiers, our method gives comparable results with the state of art methods, such as Object Bank method, for the indoor scene dataset that we used. Additionally, while well-known methods are computationally expensive, our method has a low computational cost. © 2013 IEEE.