We propose in this paper to enhance the performance of the S.I.R. [2,3,4] (Signal/semantic integration for Image Retrieval) image indexing and retrieval architecture through the integration of a query-by-example framework based on high-level image descriptions instead of their extracted low-level features. This framework features a multi-facetted conceptual model which integrates visual semantics as well as symbolic color and texture characterizations and operates on image objects (abstractions of visual entities within a physical image) in an attempt to perform querying operations beyond trivial low-level processes and region-based frameworks. Also, it manipulates a rich query language, consisting of both boolean and quantification operators, which therefore leads to optimized user interaction and increased retrieval performance. Experimental results on a test collection of 2500 color personal photographs show that our approach gives better results in terms of recall and precision measures than state-of-the-art frameworks loosely coupling keyword-based query modules and relevance feedback processes operating on low-level features.