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Instance Search with Weak Geometric Correlation Consistency


Zhenxing Zhang, Rami Albatal, Cathal Gurrin, Alan Smeaton

Publication Type: 
Refereed Conference Meeting Proceeding
Abstract. Finding object instances from large image collections is a challenging problem with many practical applications. Recent methods inspired by text retrieval achieved good results; however a re-ranking stage based on spatial verification is still required to boost performance. To improve the effectiveness of such instance retrieval systems while avoiding the computational complexity of a re-ranking stage, we explored the geometric correlations among local features and incorporate these correlations with each individual match to form a transformation consis- tency in rotation and scale space. This weak geometric correlation con- sistency can be used to effectively eliminate inconsistent feature matches and can be applied to all candidate images at a low computational cost. Experimental results on three standard evaluation benchmarks show that the proposed approach results in a substantial performance improvement compared with recent proposed methods.
Conference Name: 
Multimedia Modelling
Proceedings of Multimedia Modelling, Miami, Fl, USA
Digital Object Identifer (DOI): 
Publication Date: 
Conference Location: 
United States of America
Research Group: 
Dublin City University (DCU)
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