Learning bayesian classifiers for scene classification with a visual grammar

Abstract

Abstract-A challenging problem in image content extraction and classification is building a system that automatically learns high-level semantic interpretations of images. We describe a Bayesian framework for a visual grammar that aims to reduce the gap between low-level features and high-level user semantics. Our approach includes modeling image pixels using automatic fusion of their spectral, textural, and other ancillary attributes; segmentation of image regions using an iterative split-and-merge algorithm; and representing scenes by decomposing them into prototype regions and modeling the interactions between these regions in terms of their spatial relationships. Naive Bayes classifiers are used in the learning of models for region segmentation and classification using positive and negative examples for user-defined semantic land cover labels. The system also automatically learns representative region groups that can distinguish different scenes and builds visual grammar models. Experiments using Landsat scenes show that the visual grammar enables creation of high-level classes that cannot be modeled by individual pixels or regions. Furthermore, learning of the classifiers requires only a few training examples.

Mining of Remote Sensing Image Archives using Spatial Relationship Histograms

“…The final segmentation is obtained using an iterative split-and-merge algorithm that combines contiguous groups of pixels that are assigned to the same class. Details of the segmentation and classification algorithm can be found in [3].…”

“…Li and Narayanan [2] described a system where images are divided into tiles and are retrieved using spectral and textural statistics. Systems that support object extraction and modeling of image content based on these objects have also been developed [3,4].…”

“…For example, Shyu et al [4] developed a method that generates a spatial signature of the configuration of the objects in an image tile. In previous work [3], we developed automatic methods for extraction of topological, distance-based and relative position-based relationships between region pairs, and successfully used such relationships for image classification and retrieval in scenarios that cannot be expressed by traditional pixel-and regionbased approaches. Then, in [5], we modeled image scenes using attributed relational graphs that combine region class information and spatial arrangements, and formulated image retrieval as a relational graph matching problem.…”

Modeling of Remote Sensing Image Content Using Attributed Relational Graphs

“…The segmentation approach we have used in this work consists of pixel-based classification and an iterative split-and-merge algorithm [9]. Bayesian classifiers that fuse information from multiple features are used to assign each pixel to one of these classes.…”

“…These computations can be significantly simplified by applying a coarse-to-fine search to find region pairs that have a potential overlap or are very close to each other. In previous work [8,9], we used brute force comparisons of region pairs within smaller tiles obtained by dividing the original scene into manageable sized images. However, regions that occupy multiple tiles may not be handled correctly after that division.…”