Hefei University of Technology

About: Hefei University of Technology is a education organization based out in Hefei, China. It is known for research contribution in the topics: Computer science & Microstructure. The organization has 28093 authors who have published 24935 publications receiving 324989 citations.

Sequential Video VLAD: Training the Aggregation Locally and Temporally

Abstract: As characterizing videos simultaneously from spatial and temporal cues has been shown crucial for the video analysis, the combination of convolutional neural networks and recurrent neural networks, i.e., recurrent convolution networks (RCNs), should be a native framework for learning the spatio-temporal video features. In this paper, we develop a novel sequential vector of locally aggregated descriptor (VLAD) layer, named SeqVLAD, to combine a trainable VLAD encoding process and the RCNs architecture into a whole framework. In particular, sequential convolutional feature maps extracted from successive video frames are fed into the RCNs to learn soft spatio-temporal assignment parameters, so as to aggregate not only detailed spatial information in separate video frames but also fine motion information in successive video frames. Moreover, we improve the gated recurrent unit (GRU) of RCNs by sharing the input-to-hidden parameters and propose an improved GRU-RCN architecture named shared GRU-RCN (SGRU-RCN). Thus, our SGRU-RCN has a fewer parameters and a less possibility of overfitting. In experiments, we evaluate SeqVLAD with the tasks of video captioning and video action recognition. Experimental results on Microsoft Research Video Description Corpus, Montreal Video Annotation Dataset, UCF101, and HMDB51 demonstrate the effectiveness and good performance of our method.

Coverless Information Hiding Based on Robust Image Hashing

Abstract: Traditional image steganography modifies the content of the image more or less, it is hard to resist the detection of image steganalysis tools. New kind of steganography methods, coverless steganography methods, attract research attention recently due to its virtue of do not modify the content of the stego image at all. In this paper, we propose a new coverless steganography method based on robust image hashing. Firstly, we design an effective and stable image hash by using the orientation information of the SIFT feature points. Then the local image database is created and the corresponding hash values of these images in the database are computed. Secondly, the secret message is divided into segments with the same length as the hash sequences. And a series of images are chosen from the image database by matching the secret information segments and the hash sequences of all the images. Finally, these images are transmitted as the carriers of the secret information. When the receiver receives these images, the secret information is extracted by using the shared hash method. Due to the characteristics that SIFT features can resist common image attacks in a certain extent, the secret information corresponding to the hash has strong robustness. To improve the retrieval and matching efficiency of the hashing system, an inverted index of quadtree structure is designed. Compared with the traditional image steganography, this method does not modify the content of the image itself, therefore, can effectively resist steganalysis tools. Furthermore, we compare the proposed method with state-of-art coverless steganography method which also based on image hash, and experimental results show that our method has higher capacity, robustness and security than the method proposed in [15].