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This study investigates the propagation characteristics of rectangular Hermite-Gaussian array beams (RHGABs) through the oceanic and atmospheric turbulence considering ocean waves. Firstly, a theoretical model for the RHGABs in seawater-to-air propagation path is proposed. Secondly, we derive the expressions of the mean squared beam width and angular spread of RHGABs by using the extended Huygens-Fresnel principle and Rytov approximation. Finally, the effects of turbulent mediums on the RHGABs spreading are studied by analyzing the Rayleigh range. Our findings suggest that the Rayleigh range increases with the beam order of RHGABs. And the mean squared beam width of RHGABs in oceanic turbulence increases significantly higher than that in atmospheric turbulence. The results of this research can be applied to underwater optical communication systems.

Attribute learning has improved the performance in scene understanding and scene recognition. However, there are many attributes described by words or short texts in a static scene and traffic crowd scene. If there are two similar scenes, the semantic relationship topology structures of corresponding attribute groups of the two scenes are also homogeneity. But it is difficult to learn a semantic relation topology projection across semantic text data and visual data. To solve the problem, we construct approximate homeomorphism mapping based on the scene attributes semantic relational regularization. Hence, we propose a novel attribute semantic topological relationship regularization based scene attribute semantic learning(ARSL) method for scene semantic understanding. We establish a transport and travel scene recognition model based on attribute semantic features which are achieved by the proposed ARSL algorithm. In order to verify the proposed method, the experiments are implemented on the static transport-travel scene dataset and dynamic transport-travel crowds scene dataset respectively. The static transport-travel scene dataset is constructed by the SUN Attribute dataset including images and texts. However, the dynamic transport-travel crowds scene dataset is constructed through the WWW Crowd dataset including videos and texts, and the dynamic transport-travel crowd scene dataset is named as the WWW Crowd-Sub dataset. The performances of the proposed method are improved by 38.48% and 17.51% on the SUN Attribute dataset and WWW Crowd-Sub dataset respectively. The experimental results on the SUN Attribute dataset and WWW Crowd-Sub dataset demonstrate that the proposed approach has superior performance compared to state of the art. It can be demonstrated that the performance of the proposed ARSL method is effective against static transport-travel scene and dynamic transport-travel crowd scene.

Cross-media hashing is used to handle both cross-media representation and indexing simultaneously. Most existing methods attempt to bridge the semantic gap by maximizing the correlation of heterogeneous instances describing the same information object. Although these methods guarantee that such instances are close in the commonly shared space, instances describing different objects but the same category may be scattered. We propose a new cross-media hashing scheme, multiview cross-media hashing with semantic consistency (MCMHSC), to address this problem. By fully exploiting the semantic correlation and complementary information among objects, MCMHSC builds discriminative hashing codes. Experiments on two public benchmark datasets show that our proposed scheme achieves comparable or better performance compared to state-of-the-art methods in terms of accuracy and time complexity. [ABSTRACT FROM AUTHOR]

Recently, convolutional neural network (CNN) visual features have demonstrated their powerful ability as a universal representation for various recognition tasks. In this paper, cross-modal retrieval with CNN visual features is implemented with several classic methods. Specifically, off-the-shelf CNN visual features are extracted from the CNN model, which is pretrained on ImageNet with more than one million images from 1000 object categories, as a generic image representation to tackle cross-modal retrieval. To further enhance the representational ability of CNN visual features, based on the pretrained CNN model on ImageNet, a fine-tuning step is performed by using the open source Caffe CNN library for each target data set. Besides, we propose a deep semantic matching method to address the cross-modal retrieval problem with respect to samples which are annotated with one or multiple labels. Extensive experiments on five popular publicly available data sets well demonstrate the superiority of CNN visual features for cross-modal retrieval. [ABSTRACT FROM PUBLISHER]