On Conditional Random Field And Association Analysis For Scene Classification

With fast development of image sensor, computer science, network and storage, theresearch of image process has been paid more and more attention by scholars. Image is animportant information carrier because its abundant and connotative. Images’ descriptionand classification are two key technologies for images’ effective management and quickretrieval. The whole process of scene classification contains segmentation, labeling, objectclassification and semantic derivation. In this paper, the problem of segmentation, objectclassification and scene derivation have been researched.As any of segment algorithm can solve all situations well. We propose to getmulti-scale segment results to give more cues for object classification. We use semantictexton forest to get over-cut results first. Then we propose a tree structure to merge oddparts. Finally, we extract two level segment results.We set objects in scene as two parts: foreground objects and background objects.Using two level segment results and two level objects, we propose a conditional randomfields to describe the relationship amo ng these four levels. We then get the result of objectclassification through this model.Association analysis algorithm has been used to mine the relationship betweenobjects and scene. Through establishing frequent item of each scene category, we can getthe final scene result. As in some cases it may be information missing or confusion, wepropose a method to solve it.