Browsing by Subject "Context modeling"

Now showing 1 - 7 of 7

Open Access
Automatic detection of compound structures by joint selection of region groups from a hierarchical segmentation
(Institute of Electrical and Electronics Engineers, 2016) Akçay, H. G.; Aksoy, S.
A challenging problem in remote sensing image analysis is the detection of heterogeneous compound structures such as different types of residential, industrial, and agricultural areas that are composed of spatial arrangements of simple primitive objects such as buildings and trees. We describe a generic method for the modeling and detection of compound structures that involve arrangements of an unknown number of primitives in large scenes. The modeling process starts with a single example structure, considers the primitive objects as random variables, builds a contextual model of their arrangements using a Markov random field, and learns the parameters of this model via sampling from the corresponding maximum entropy distribution. The detection task is formulated as the selection of multiple subsets of candidate regions from a hierarchical segmentation where each set of selected regions constitutes an instance of the example compound structure. The combinatorial selection problem is solved by the joint sampling of groups of regions by maximizing the likelihood of their individual appearances and relative spatial arrangements. Experiments using very high spatial resolution images show that the proposed method can effectively localize an unknown number of instances of different compound structures that cannot be detected by using spectral and shape features alone.
Open Access
Automatic detection of compound structures by joint selection of region groups from multiple hierarchical segmentations
(2016-09) Akçay, Hüseyin Gökhan
A challenging problem in remote sensing image interpretation is the detection of heterogeneous compound structures such as different types of residential, industrial, and agricultural areas that are comprised of spatial arrangements of simple primitive objects such as buildings and trees. We describe a generic method for the modeling and detection of compound structures that involve arrangements of unknown number of primitives appearing in different primitive object layers in large scenes. The modeling process starts with example structures, considers the primitive objects as random variables, builds a contextual model of their arrangements using a Markov random field, and learns the parameters of this model via sampling from the corresponding maximum entropy distribution. The detection task is reduced to the selection of multiple subsets of candidate regions from multiple hierarchical segmentations corresponding to different primitive object layers where each set of selected regions constitutes an instance of the example compound structures. The combinatorial selection problem is solved by joint sampling of groups of regions by maximizing the likelihood of their individual appearances and relative spatial arrangements under the model learned from the example structures of interest. Moreover, we incorporate linear equality and inequality constraints on the candidate regions to prevent the co-selection of redundant overlapping regions and to enforce a particular spatial layout that must be respected by the selected regions. The constrained selection problem is formulated as a linearly constrained quadratic program that is solved via a variant of the primal-dual algorithm called the Difference of Convex algorithm by rewriting the non-convex program as the difference of two convex programs. Extensive experiments using very high spatial resolution images show that the proposed method can provide good localization of unknown number of instances of different compound structures that cannot be detected by using spectral and shape features alone.
Open Access
Detection of compound structures by region group selection from hierarchical segmentations
(IEEE, 2016-07) Akçay, H. Gökhan; Aksoy, Selim
Detection of compound structures that are comprised of different arrangements of simpler primitive objects has been a challenging problem as commonly used bag-of-words models are limited in capturing spatial information. We have developed a generic method that considers the primitive objects as random variables, builds a contextual model of their arrangements using a Markov random field, and detects new instances of compound structures through automatic selection of subsets of candidate regions from a hierarchical segmentation by maximizing the likelihood of their individual appearances and relative spatial arrangements. In this paper, we extend the model to handle different types of primitive objects that come from multiple hierarchical segmentations. Results are shown for the detection of different types of housing estates in a WorldView-2 image. © 2016 IEEE.
Open Access
Detection of compound structures using a gaussian mixture model with spectral and spatial constraints
(Institute of Electrical and Electronics Engineers Inc., 2014) Arı, C.; Aksoy, S.
Increasing spectral and spatial resolution of new-generation remotely sensed images necessitate the joint use of both types of information for detection and classification tasks. This paper describes a new approach for detecting heterogeneous compound structures such as different types of residential, agricultural, commercial, and industrial areas that are comprised of spatial arrangements of primitive objects such as buildings, roads, and trees. The proposed approach uses Gaussian mixture models (GMMs), in which the individual Gaussian components model the spectral and shape characteristics of the individual primitives and an associated layout model is used to model their spatial arrangements. We propose a novel expectation-maximization (EM) algorithm that solves the detection problem using constrained optimization. The input is an example structure of interest that is used to estimate a reference GMM and construct spectral and spatial constraints. Then, the EM algorithm fits a new GMM to the target image data so that the pixels with high likelihoods of being similar to the Gaussian object models while satisfying the spatial layout constraints are identified without any requirement for region segmentation. Experiments using WorldView-2 images show that the proposed method can detect high-level structures that cannot be modeled using traditional techniques. © 1980-2012 IEEE.
Open Access
Detection of compound structures using multiple hierarchical segmentations
(IEEE, 2014) Akçay, Hüseyin Gökhan; Aksoy, Selim
In this paper, we present a method for automatic compound structure detection in high-resolution images. Given a query compound structure, our aim is to detect coherent regions with similar spatial arrangement and characteristics in multiple hierarchical segmentations. A Markov random field is constructed by representing query regions as variables and connecting the vertices that are spatially close by edges. Then, a maximum entropy distribution is assumed over the query region process and selection of similar region processes among a set of region hierarchies is achieved by maximizing the query model. Experiments using WorldView-2 images show the efficiency of probabilistic modeling of compound structures. © 2014 IEEE.
Open Access
SAR image reconstruction by EMMP algorithm
(IEEE, 2012-04) Uǧur, Salih; Arıkan, Orhan; Gürbüz, A. C.
In this work, EMMP algorithm is used to solve the SAR image reconstruction problem which is modelled in the compressed sensing context. It is found that the sparsity parameter of the target region is an important parameter determining the quality of the output image. The proposed method is applied to the real SAR data and provided high quality outputs. © 2012 IEEE.
Open Access
Time-aware and context-sensitive ensemble learning for sequential data
(Institute of Electrical and Electronics Engineers, 2023-09-26) Fazla, Arda; Aydın, Mustafa E.; Kozat, Suleyman Serdar
We investigate sequential time series data through ensemble learning. Conventional ensemble algorithms and the recently introduced ones have provided significant performance improvements in widely publicized time series prediction competitions for stationary data. However, recent studies are inadequate in capturing the temporally varying statistics for non-stationary data. To this end, we introduce a novel approach using a meta learner that effectively combines base learners in both a time varying and context-dependent manner. Our approach is based on solving a weight optimization problem that minimizes a specific loss function with constraints on the linear combination of the base learners. The constraints are theoretically analyzed under known statistics and integrated into the learning procedure of the meta-learner as part of the optimization in an automated manner. We demonstrate significant performance improvements on real-life data and well-known competition datasets over the widely used conventional ensemble methods and the state-ofthe-art forecasting methods in the machine learning literature. Furthermore, we openly share the source code of our method to facilitate further research and comparison.