A Novel Sketch-Based Three-Dimensional Shape Retrieval Method Using Multi-View Convolutional Neural Network

Retrieving 3D models by adopting hand-drawn sketches to be the input has turned out to be a popular study topic. Most current methods are based on manually selected features and the best view produced for 3D model calculations. However, there are many problems with these methods such as distortion. For the purpose of dealing with such issues, this paper proposes a novel feature representation method to select the projection view and adapt the maxout network to the extended Siamese network architecture. In addition, the strategy is able to handle the over-fitting issue of convolutional neural networks (CNN) and mitigate the discrepancies between the 3D shape domain and the sketch. A pre-trained AlexNet was used to sketch the extract features. For 3D shapes, multiple 2D views were compiled into compact feature vectors using pre-trained multi-view CNNs. Then the Siamese convolutional neural networks were learnt for transforming the two domains’ original characteristics into nonlinear feature space, which mitigated the domain discrepancy and kept the discriminations. Two large data sets were used for experiments, and the experimental results show that the method is superior to the prior art methods in accuracy.

Syntactic Neutralization in Double Object Constructions

This article synthesizes the ‘‘alternative projection’’ view of the alternation between the DP + DP and DP + PP complement frames of English double object verbs, according to which the alternants are basegenerated as such, with a transformational account that claims that the DP + PP frame may be derived from the DP + DP frame. For some verbs allowing multiple complements, the DP + PP frame is syntactically ambiguous between a base-generated locative construction and a derivative of the possessive syntax associated with the DP + DP frame. Evidence from the distribution of purpose clauses motivates this conclusion, as do asymmetries in restrictions on animacy and idiom formation in the two frames.