Multi-feature query language for image classification

Despite the major effort put into the creation of Content-Based Image Retrieval (CBIR) system
during the last decade, the solutions available are still not satisfying for generic purposes.
The most severe issue seems to be the so-called “semantic gap”. It is feasible to define and use
domain specific feature vectors on a low level and use this information for a similarity based retrieval.
Yet, mapping these to higher level semantics remains dicult. This research investigates
a domain-independent way of automatized image categorization. A CBIR query language is constructed
to build query-like descriptors for each category to be learned. The proposed learning
algorithm is based on decision-trees. The resulting descriptors are aimed to be understandable
and modifiable by expert users. A case-study is presented to support these claims.