| Due to the huge amount of potentially interesting documents available over the Internet, searching for relevant information has become very difficult. Since image and video are a major source of these data, grouping images into (semantically) meaningful categories using low-level visual features is an important (and challenging) problem in content-based image retrieval. Using Bayesian classifiers, we attempt to capture high-level concepts from low-level image features. Specifically, we have developed Bayesian classifiers for semantic image classification (indoor vs. outdoor, city vs. landscape, and sunset vs. forest vs. mountain), image orientation detection, and object detection (detecting regions of sky and vegetation in outdoor images). We demonstrate that a small codebook (the optimal codebook size is selected using a modified MDL criterion) extracted from a learning vector quantizer can be used to estimate the class-conditional densities of the observed features needed for image classification. We have developed an incremental learning paradigm, a feature selection scheme, a rejection scheme, and a classifier combination strategy using bagging to improve classifier performance. Empirical results on a large database (∼24,000 images) show that semantic categorization and organization of the database using the proposed classification schemes improves both retrieval accuracy and efficiency. |
