Integrating visual ontologies and wavelets for image content retrieval

One of the challenging problems for image content retrieval is how to represent semantics of images. We have proposed a new representation for image contents using visual ontologies and wavelets. We will discuss how to represent the shape of objects/regions of interest, colour distribution and texture of the images integrating visual ontologies with wavelets. Our ideas are: (1) propose a set of meta ontologies and semantic descriptors (graphic and symbolic) for visual ontologies; (2) using visual ontologies and most significant coefficients of a 2D multiresolution wavelet transform to represent image contents. The visual ontologies are represented by a set of descriptors, relations, logic operators, and functions; (3) using a multiresolution wavelet transform to extract the colour and texture features of target images and map it into relevant visual ontologies. The query images can also be represented as visual ontologies or most significant coefficients of a 2-D multiresolution wavelet; (4) a spatial query can be integrated with other visual ontologies. The initial experiments have shown encouraging results