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In order to achieve content-based binocular stereoscopic image or video retrieval efficiently, a feature indexing algorithm based on hybrid grid multiple suffix tree and hierarchical clustering is proposed. With the RGB-D image model, the shape features of depth map obtained from the matching of binocular stereoscopic left image and right image and the color features of left image are extracted respectively. The features are quantified and hashed, and the optimized underlying features are sorted as leaf nodes of hierarchical indexing. Then the shape and color feature values of leaf nodes are mapped to the two-dimensional coordinates, and the 2D feature points are put into different grid hash areas respectively by clustering and labeled with multiple suffix tree. Furthermore, to construct the global index, a pointer to an array of clustering grid center point is defined according to the computation of grid area feature values. The experimental results show that compared to the double grid suffix tree and typical stereoscopic image feature indexing structure, the proposed algorithm can effectively reduce the indexing construction complexity While maintaining high recall, it can also greatly improve the query efficiency, which can better realize the feature indexing of binocular stereoscopic images or videos.

Stereoscopic Image Feature Indexing Based on Hybrid Grid Multiple Suffix Tree and Hierarchical Clustering