In this paper, a gaze-based Relevance Feedback (RF) approach to region-based image retrieval is presented. Fundamental idea of the proposed method comprises the iterative estimation of the real-world objects (or their constituent parts) that are of interest to the user and the subsequent exploitation of this information for refining the image retrieval results. Primary novelties of this work are: a) the introduction of a new set of gaze features for realizing user’s relevance assessment prediction at region-level, and b) the design of a time-efficient and effective object-based RF framework for image retrieval. Regarding the interpretation of the gaze signal, a novel set of features is introduced by formalizing the problem under a mathematical perspective, contrary to the exclusive use of explicitly defined features that are in principle derived from the psychology domain. Apart from the temporal attributes, the proposed features also represent the spatial characteristics of the gaze signal, which have not been extensively studied in the literature so far. On the other hand, the developed object-based RF mechanism aims at overcoming the main limitation of regionbased RF approaches, i.e. the frequently inaccurate estimation of the regions of interest in the retrieved images. Moreover, the incorporation of a single-camera image processing-based gaze tracker makes the overall system cost efficient and portable. As it is shown by the experimental evaluation, the proposed method outperforms representative global- and region-based explicit RF approaches, using a challenging general-purpose image dataset.