Similarity Calculation of 3D Model By Integrating Improved ACO Into HNN

Abstract

It is important for 3D model retrieval to compute similarity between two models accurately. In order to calculate two models' similarity, their face matching scheme need be found. Hopfield neural network (HNN) and ant colony optimization (ACO) algorithm can be used to match source faces and target ones. But, they all drop into local optimum easily and can not get optimal face matching scheme. This paper proposes a new method of computing model similarity by integrating improved ACO algorithm into HNN. Shape similarity between source face and target one is calculated based on the difference of face's edge number. Structure similarity is computed according to adjacency correspondence relationship between source face and target one. Two faces' similarity is calculated based on shape similarity and structure similarity, which is introduced into transfer probability. Indirect expectation heuristic is defined to improve ACO algorithm. Then, improved ACO algorithm is integrated into HNN to search for optimal face matching scheme. Model similarity is computed based on optimal sequence of face pairs. Experimental results show that compared with improved ACO algorithm, the proposed method improves the ranking effect of 14.29% of models, which can measure two models' difference effectively.

Keywords

• 3D model
• Hopfield neural network
• ant colony optimization
• shape similarity
• structure similarity
• indirect expectation heuristic