Feature Combination and Relevance Feedback for 3D Model Retrieval

Abstract

Retrieval of 3D models have attracted much research interest, and many types of shape features have been proposed. In this paper, we describe a novel approach of combining the feature types for 3D model retrieval and relevance feedback processing.Our approach performs query processing using pre-computed pairwise distances between objects measured according to various feature types. Experimental tests show that this approach performs better than retrieval by individual feature type.

PANORAMA: A 3D Shape Descriptor Based on Panoramic Views for Unsupervised 3D Object Retrieval

“…Apart from a few studies that incorporated RF in 3D object retrieval [42], [43], [44], [45], [46], [47], [48], LRF has only lately been examined in [5].…”

Comparison of Dimension Reduction Methods for Database-Adaptive 3D Model Retrieval

“…Interactive relevance feedback, a form of on-line interactive learning, has been explored by several re-searchers for 3D model retrieval [8,1,15,16]. Elad et al is among the first to apply Support Vector Machines (SVM) learning in an on-line learning setting to improve 3D model retrieval [8].…”

Semantic force relevance feedback, content-free 3D object retrieval and annotation propagation: bridging the gap and beyond

“…In the typical situation after the user specifies his/her query object, the system retrieves a ranked list of the most similar objects according to the similarity metric described by (4). The user observes this list and gives scores to arbitrarily many 3D objects.…”

“…In [26], the Kernel Principal Component Analysis (KPCA) [39] and the Linear Discriminant Analysis (LDA) [13] algorithms are implemented in order to improve the retrieval accuracy during RF iterations. A different approach is given in [4], where query processing and RF are performed using pre-computed, pair-wise distances between objects.…”