Query By Visual Example (QBVE) has been widely exploited in image retrieval. Global visual similarity as well as points of interest matching have proven their efficiency when example image/region is available. If starting image is missing, the Query By Visual Thesaurus (QBVT) paradigm offsets it by allowing the user to compose his mental query image through visual patches summarizing the region database. In this paper, we propose to enrich the paradigm of mental image search by constructing a reliable visual thesaurus of the regions provided by a new coherence criterion. Our criterion encapsulates the local distribution of detected points of interest within a region. It leads to semantic labelling of regions categories using points spatial topology. Our point-based criterion has been validated on a generic image database combining homogenous regions as well as irregularly and fully textured patterns.