IRM: integrated region matching for image retrieval

By Jia Li, James Ze Wang, Gio Wiederhold. ACM Multimedia, 2000

442 citations recorded by the source corpus.

Content-based image retrieval using region segmentation has been an active research area. We present IRM (Integrated Region Matching), a novel similarity measure for region-based image similarity comparison. The targeted image retrieval systems represent an image by a set of regions, roughly corresponding to objects, which are characterized by features reflecting color, texture, shape, and location properties. The IRM measure for evaluating overall similarity between images incorporates properties of all the regions in the images by a region-matching scheme. Compared with retrieval based on individual regions, the overall similarity approach reduces the influence of inaccurate segmentation, helps to clarify the semantics of a particular region, and enables a simple querying interface for region-based image retrieval systems. The IRM has been implemented as a part of our experimental SIMPLIcity image retrieval system. The application to a database of about 200,000 general-purpose images shows exceptional robustness to image alterations such as intensity variation, sharpness variation, color distortions, shape distortions, cropping, shifting, and rotation. Compared with several existing systems, our system in general achieves more accurate retrieval at higher speed.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/37217f11b576b2b43f245295553f1aca413d59ae

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In Computer Vision and Pattern Recognition

Citations in the corpus

A compact and efficient image retrieval approach based on border/interior pixel classification (2002, unknown): “[5, 13]).”
Image retrieval: Ideas, influences, and trends of the new age (2008, unknown): “… 2002], to involve every generated segment of an image in the matching process to obtain soft similarity measures [Wang et al. 2001], or to characterize spatial arrangement of color and texture using block-based 2D multiresolution hidden Markov models (MHMMs) [Li et al. 2000; Li and Wang 2003].”
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries (2000, background): “Details regarding to the de nitions of the IRM similarity measure and the distance between two regions are referred to [7].”
A Region-Based Fuzzy Feature Matching Approach to Content-Based Image Retrieval (2002, methodology): “Content-based image retrieval (CBIR) is aimed at eÆcient retrieval of relevant images from large image databases based on automatically derived imagery features.”
OPTIMOL: Automatic Online Picture Collection via Incremental Model Learning (2007, methodology): “Image Retrieval from the Web Content-based image retrieval (CBIR) (Zhou and Huang 2002; Deng 2001; Carson et al. 1999; Li et al. 2000; Chen et al. 2003; Jain and Vailaya 1996; Barnard and Forsyth 2001; Barnard et al. 2003; Jeon et al. 2003) has been long an active field of research.”