Automated detection of objects using multiple hierarchical segmentations
Date
2007-07Source Title
International Geoscience and Remote Sensing Symposium (IGARSS), 2007
Publisher
IEEE
Pages
1468 - 1471
Language
English
Type
Conference PaperItem Usage Stats
172
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125
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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 detectionMultiple hierarchical segmentations
Structural information
Algorithms
Image segmentation
Information dissemination
Probability
Object recognition