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A Spatiotemporal Attention-Based Method for Geo-Referenced Video Coding
Jiangfan Feng,Yi Zhu,Haibin Hu 보안공학연구지원센터 2014 International Journal of Signal Processing, Image Vol.7 No.5
This paper focuses on the problem that video-GIS has a huge amount of data, which leads to high transmission resource consumption, and introduced attention calculation model to video GIS coding to optimize the coding method so the compression efficiency will be improved as well. Specifically, on the one hand, it will use optical flow technique to calculate the specific location and motion vector of the movement point set of each frame, and reduce the amount of the movement point set integrate with the features of video GIS, then calculate the mask matrix of the foreground movement, and finally compute the Discrete Cosine Transform (DCT) compression integrate with the mask matrix to realize the optimization of the frame macro block level coding. On the other hand, it will calculate the attention of the video frame using the specific location and the motion vector of the movement point set as the primary indicator, and optimize the coding of the video of frame-level according to the calculation result. By experimental verification, the efficiency of video GIS compression is enhanced by the introduction of the features of the video GIS and the cognitive attention theory
Cross-Media Retrieval using Probabilistic Model of Automatic Image Annotation
Ying Xia,YunLong Wu,JiangFan Feng 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.4
In recent years, automatic image annotation (AIA) has been applied to cross-media retrieval usually due to its advantage of mining correlations of images and annotation texts efficiently. However, some AIA methods just annotate images as a unit and the accuracy of annotation may not be acceptable. In this paper, we propose a kind of probabilistic model which may assign keywords to an un-annotated image automatically based on a training dataset of images. Images in the training dataset are segmented into regions and a kind of vocabulary called blob is used to represent these image regions. Blobs are generated by using K-Means algorithm to cluster these image regions. Through this model, we can predict the probability of assigning a keyword into a blob. After the accomplishment of annotation, a keyword corresponds to one image region. Furthermore, the feature vectors of text documents are generated by TF.IDF method and images’ automatic annotation information is used to retrieve relevant text documents. Experiments on the IAPR TC-12 dataset and 500 Wikipedia webpages about landscape show the usefulness of applying probabilistic model of AIA to the cross-media retrieval.