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Automated Detection of Objects Using Multiple Hierarchical Segmentations
 

Summary: Automated Detection of Objects Using
Multiple Hierarchical Segmentations
H. G¨okhan Akc¸ay and Selim Aksoy
Department of Computer Engineering
Bilkent University
Bilkent, 06800, Ankara, Turkey
{akcay,saksoy}@cs.bilkent.edu.tr
Abstract--We introduce an unsupervised method that com-
bines 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 segmen-
tations and multiple principal components. The segments are

  

Source: Aksoy, Selim - Department of Computer Engineering, Bilkent University

 

Collections: Computer Technologies and Information Sciences