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      • Department of Computer Engineering
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      Automated detection of objects using multiple hierarchical segmentations

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      Author(s)
      Akçay H. Gökhan
      Aksoy, Selim
      Date
      2007-07
      Source Title
      International Geoscience and Remote Sensing Symposium (IGARSS), 2007
      Publisher
      IEEE
      Pages
      1468 - 1471
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
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      Abstract
      We introduce an unsupervised method that combines both spectral and structural information for automatic object detection. First, a segmentation hierarchy is constructed by combining structural information extracted by morphological processing with spectral information summarized using principal components analysis. Then, segments that maximize a measure consisting of spectral homogeneity and neighborhood connectivity are selected as candidate structures for object detection. Given the observation that different structures appear more clearly in different principal components, we present an algorithm that is based on probabilistic Latent Semantic Analysis (PLSA) for grouping the candidate segments belonging to multiple segmentations and multiple principal components. The segments are modeled using their spectral content and the PLSA algorithm builds object models by learning the object-conditional probability distributions. Labeling of a segment is done by computing the similarity of its spectral distribution to the distribution of object models using Kullback-Leibler divergence. Experiments on two data sets show that our method is able to automatically detect, group, and label segments belonging to the same object classes. © 2007 IEEE.
      Keywords
      Automated detection
      Multiple hierarchical segmentations
      Structural information
      Algorithms
      Image segmentation
      Information dissemination
      Probability
      Object recognition
      Permalink
      http://hdl.handle.net/11693/26981
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/IGARSS.2007.4423085
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      • Department of Computer Engineering 1435
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