Genetic Optimization for Associative Semantic Ranking Models of Satellite Images by Land Cover

Abstract

Abstract:Associative methods for content-based image ranking by semantics are attractive due to the similarity of generated models to human models of understanding. Although they tend to return results that are better understood by image analysts, the induction of these models is difficult to build due to factors that affect training complexity, such as coexistence of visual patterns in same images, over-fitting or under-fitting and semantic representation differences among image analysts. This article proposes a methodology to reduce the complexity of ranking satellite images for associative methods. Our approach employs genetic operations to provide faster and more accurate models for ranking by semantic using low level features. The added accuracy is provided by a reduction in the likelihood to reach local minima or to overfit. The experiments show that, using genetic optimization, associative methods perform better or at similar levels as state-of-the-art ensemble methods for ranking. The mean average precision (MAP) of ranking by semantic was improved by 14% over similar associative methods that use other optimization techniques while maintaining smaller size for each semantic model.

A neotropical Miocene pollen database employing image‐based search and semantic modeling

“…Three segmentation results (highlighted red contours superposed on original grain images) are shown per each image example using different weight confi gurations for hue ( w H ) and saturation ( w S ) channels. http://www.bioone.org/loi/apps the mathematical formulas detailed in Barb and Shyu (2010) and Barb and Kilicay-Ergin (2013) , each semantic representation is constructed as an association model using the concept of possibilistic c -means algorithm ( Krishnapuram and Keller, 1993 ) based on low-level visual features. This process is called semantic modeling.…”

Predicting Relevant Change in High Resolution Satellite Imagery

“…In [16], Barb and Kilicay-Ergin developed semantic models using genetic optimization of low-level image features. Other examples of applying data mining algorithms to remote sensing imagery include mining temporal-spatial information [17] and using association rules to extract information from the gaze patterns of individuals viewing satellite imagery [18].…”