Contextual Analysis of Textured Scene Images
Markus Turtinen, Matti Pietikäinen · 2006
Classifying image regions into one of several pre-defined semantic categories is a typical image understanding problem. Different image regions and object types might have very similar color or texture characteristics making it difficult to categorize them. Without contextual information it is often impossible to find reasonable semantic labeling for outdoor images. In this paper, we combine an efficient SVM-based local classifier with the conditional random field framework to incorporate spatial contex information to the classification. The images are represented with powerful local texture features. Then a discriminative multiclass model for finding good labeling for the image is learned. The performance of the method was evaluated with two different datasets. The approach was also shown to be useful in more general image retrieval and annotation tasks based on classification. 1