This study presents a content-based image retrieval system IMALBUM based on local region of interest called object of interest (OOI). Each segmented or user-selected OOI is indexed with new local adapted descriptors associated to color, texture, and shape features. This local approach is an efficient way to associate the local semantic content with low-level descriptors (color, texture, shape, etc.) computed on regions selected by the user. So the user actively takes part in the indexing process (offline) and can use a selected OOI as a query for the retrieval system (online). The IMALBUM system proposes original functionalities. A visual navigation tool allows to surf in the image database when the user has no precise idea of what he is really searching for in the database. Furthermore, when an OOI is selected as a query for retrieval, a semantic content identification tool indicates to the user the probable class of this unknown object. The performance of these different tools are evaluated on different databases.