Object-Based Directional Query Processing in Spatial Databases
IEEE Transactions on Knowledge and Data Engineering
Image indexing and similarity retrieval based on spatial relationship model
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Introduction to multimedia and mobile agents
2D Z-string: a new spatial knowledge representation for image databases
Pattern Recognition Letters
Pattern Recognition Letters
IEEE Transactions on Knowledge and Data Engineering
Solving multi-instance problems with classifier ensemble based on constructive clustering
Knowledge and Information Systems - Special Issue on Mining Low-Quality Data
Information Processing and Management: an International Journal - Special issue: AIRS2005: Information retrieval research in Asia
Graph-based representation for similarity retrieval of symbolic images
Data & Knowledge Engineering
Journal of Visual Languages and Computing
Word sense disambiguation with pictures
Artificial Intelligence - Special volume on connecting language to the world
Real-valued multiple-instance learning with queries
Journal of Computer and System Sciences
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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.