Multiple-instance image database retrieval by spatial similarity based on Interval Neighbor Group

Abstract

In this paper, a multiple-instance image retrieval system incorporating a general spatial similarity measure is proposed. A multiple-instance learning is employed to summarize the commonality of spatial features among positive and negative example images. The general spatial similarity measure evaluates the degree of similarity between matching atomic spatial relations present in the maximum common object set of the query and a database image based on their nodal distance in an Interval Neighbor Group (ING). The shorter the distance, the higher degree of similarity, while a longer one, a lower degree of similarity. An ensemble similarity measure, derived from the spatial relations of all constituent objects in the query and a database image, will then integrate these atomic spatial similarity assessments and give an overall similarity value between two images. Therefore, images in a database can be quantitatively ranked according to the degree of ensemble spatial similarity with the query. In order to demonstrate the feasibility of the proposed approach, two sets of test for querying an image database are performed, namely, single-instance v.s. multiple-instance retrieval by employing the RSS-ING scheme proposed and the RSS-ING scheme v.s. 2D Be-string similarity method incorporating identical multiple-instance learning. The ING-based spatial similarity measure with fine granularity, combined with the utilization of a multiple-instance learning paradigm to forge a unified query key, produces desirable retrieval results that better match user's expectation.