MULTI-FEATURE INTEGRATION WITH RELEVANCE FEEDBACK ON 3D MODEL SIMILARITY RETRIEVAL
Keywords:
3D model similarity retrieval, feature integration, relevance feedback, multipoint queriesAbstract
In this paper, we combine the use of Reduced Feature Vector Integration (RFI) and Distance Integration (DI) with Relevance Feedback (RF) on 3D model similarity retrieval. The RFI outperforms the individual FVs and gives high probability of providing relevant objects, other than the query itself, on the limited-size of display window. Therefore, user may select as many relevant objects as possible just after the initial query for the next RF iteration. In order to deal with the user’s feedback, we have used and extended an RF algorithm, which enhances the precision by employing multipoint queries and estimating feature relevance derived from both the variance of the distance of relevant objects and the maximum rank of them. In addition, an Extended Exclusion Set (EES) incorporating with Exclusion Set (ES) is introduced. Using EES and ES, the RF algorithm pushes prospectively irrelevant objects away from the queries. By utilizing both approaches, the small number of RF iterations significantly improves the retrieval precision.
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