TOWARDS SEMANTIC-BASED IMAGE ANNOTATION By Andres Dorado Supervised By

This research work addresses the problem of using concept-related indexing of image content as a near-automatic way to perform semantic image annotation. The main objective is to pro-vide a framework in which lexical information of visual interpretations and their components (concept-related indexes) can be used to perform content-based image annotation. Several design phases starting from the formation of an MPEG-7 learning space to the construction of a robust semantic indexer were applied. Salient features of the proposed framework for concept-related indexing of image content are: Provide a suitable combination of low-level visual features. A specific concern is on defining a structure in which MPEG-7 descriptor elements may be aggregated into feature vectors. A structure is proposed to preserve the semantics embedded in descriptors, avoid description overriding, and control the vector dimensionality using the minimum number of required elements. Unambiguous interpretation is reduced using a built-in knowledge base consisting of con-cepts organized into a restrained lexicon