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Since each visual feature only reflects a unique characteristic about a 3-dimensional (3D) model and different visual features have diverse discriminative power in model representation, it would be beneficial to fuse multiple visual features in 3D model retrieval. To this end, we propose a fast view-based 3D model retrieval framework in this article. This framework comprises two parts: the first one is an Unsupervised Multiple Feature Fusion algorithm (UMFF), which is used to learn a compact yet discriminative feature representation from the original multiple visual features; and the second one is an efficient Online Projection Learning algorithm (OPL), which is designed to fast transfer the input multiple visual features of a newcome model into its corresponding low-dimensional feature representation. In this framework, many existing ranking algorithms such as the simple distance-based ranking method can be directly adopted for sorting all 3D models in the database using the learned new feature representation and returning the top ranked models to the user. Extensive experiments on two public 3D model databases demonstrate the efficiency and the effectiveness of the proposed approach over its competitors. The proposed framework cannot only dramatically improve the retrieval performance but also reduce the computational cost in dealing with the newcome models. HighlightsAn Unsupervised Multiple Feature Fusion (UMFF) algorithm is proposed to fuse multiple features for 3D model representation.The ? 1 -graph method is used to learn roust and datum-adaptive graph to preserve local geometric structure information.An efficient Online Projection Learning (OPL) algorithm is designed to solve the out-of-sample problem.

Multiple visual features have been proposed and used in 3-dimensional (3D) model retrieval in recent years. Since each visual feature reflects a unique characteristic about the model, they have unequal discriminative power with respect to a specific category of 3D model, and they are complementary to each other in model representation. Thus, it would be beneficial to combine multiple visual features together in 3D model retrieval. In light of this, we propose an efficient Semi-supervised Multiple Feature Fusion (SMFF) method for view-based 3D model retrieval in this paper. Specifically, We first extract multiple visual features to describe both the local and global appearance characteristics of multiple 2D projected images that are generated from 3D models. Then, SMFF is adopted to learn a more compact and discriminative low-dimensional feature representation via multiple feature fusion using both the labeled and unlabeled 3D models. Once the low-dimensional features have been learned, many existing methods such as SVM and KNN can be used in the subsequent retrieval phase. Moreover, an out-of-sample extension of SMFF is provided to calculate the low-dimensional features for the newly added 3D models in linear time. Experiments on two public 3D model datasets demonstrate that using such a learned feature representation can significantly improve the performance of 3D model retrieval and the proposed method outperforms the other competitors.

In this paper, we mainly focus on the problem of quantized feedback stabilization of a discrete-time linear system with Markovian jump packet losses. Based on the packet loss dependent Lyapunov functions, the coarsest quantizer is analyzed in detail to guarantee the mean square quadratic stabilization of the system. If the system adopts the logarithmic quantizer, the necessary and sufficient conditions, which make the system mean square quadratically stabilized by a linear state feedback controller, can be converted into an algebraic Riccati equation and then into an LMI. Then based on this LMI, we obtain the infimum quantization density of the logarithmic quantizer over all dependent Lyapunov functions. Finally, a numerical example is presented to illustrate the effectiveness of the results obtained in this paper.