Spatial bayesian learning algorithms for geographic information retrieval

Abstract

An increasing amount of freely available Geographic Information System (GIS) data on the Internet has stimulated recent research into Geographic Information Retrieval (GIR). Typically, GIR looks at the problem of retrieving GIS datasets on a theme by theme basis. However in practice, themes are generally not analysed in isolation. More often than not multiple themes are required to create a map for a particular analysis task. To do this using the current GIR techniques, each theme is retrieved one by one using traditional retrieval methods and manually added to the map. To automate map creation the traditional GIR paradigm of matching a query to a single theme type must be extended to include discovering relationships between different theme types.Bayesian Inference networks can and have recently been adapted to provide a theme to theme relevance ranking scheme which can be used to automate map creation [2]. The use of Bayesian inference for GIR relies on a manually created Bayesian network. The Bayesian network contains causal probability relationships between spatial themes. The next step in using Bayesian Inference for GIR is to develop algorithms to automatically create a Bayesian network from historical data. This paper discusses a process to utilize conventional Bayesian learning algorithms in GIR. In addition, it proposes three spatial learning Bayesian network algorithms that incorporate spatial relationships between themes into the learning process. The resulting Bayesian networks were loaded into an inference engine that was used to retrieve all relevant themes given a test set of user queries. The performance of the spatial Bayesian learning algorithms were evaluated and compared to performance of conventional non-spatial Bayesian learning algorithms. This contribution will increase the performance and efficiency of knowledge extraction from GIS by allowing users to focus on interpreting data, instead of focusing on finding which data is relevant to their analysis.

Bayesian Network Integration with GIS

“…BN integration with GIS typically takes one of the four distinct forms: (1) BN-based layer combination (i.e., probabilistic map algebra) as demonstrated in Taylor (2003); (2) BN-based classification as demonstrated in Stassopoulou et al (1998) and Stassopoulou et al (1998); (3) using BNs for intelligent, spatially oriented data retrieval, as demonstrated in Walker et al (2004) and Walker et al (2005); and (4) GIS-based BN decision support system (DSS) frameworks where BN nodes are spatially represented in a GIS framework as presented by Ames et al (2005).…”

A Bayesian network-based tunable image segmentation algorithm for object recognition

“…Walker et. al also used this idea/concept in [13] to automate map creation by creating a single contiguity matrix to show the influence of neighborhood among the spatial themes.…”

“…In the context of geographical maps, Walker et al [13] proposed spatial learning BN algorithms that incorporate spatial relationships between spatial themes into the learning process. Inspired by this, in this paper we present a tunable image segmentation algorithm that uses a BN as a probabilistic graphical model to learn both visual and spatial relationships among the fragments of the OOI, which in turn, becomes evidence that is used for the process of semantically accurate segmentation of future instances of the OOI.…”

Image Indexing and Retrieval for a Vietnamese Folk Paintings Gallery

“…An example of an iconographic system is the documentation of art historical images by Waal published in 17 volumes [8]. More recently, another similar scheme by Yee et al [10] is to search images by faceted metadata which is composed of orthogonal sets of categories. The facets could be themes, artists' names, periods, locations, etc.…”

“…We have dealt with a similar problem for GIS (Geographic Information Systems), to retrieve answers to vague queries such as "I am an artistic person, find me interesting places to visit" [9,10]. In this case, we constructed a Bayesian Inference Network (BIN) which is capable of incorporating expert knowledge and spatial relationships between data sets.…”