Classic local space-time features are successful representations for action recognition in videos. However, these features always confuse object motions with camera motions, which seriously affect the accuracy of acti...Classic local space-time features are successful representations for action recognition in videos. However, these features always confuse object motions with camera motions, which seriously affect the accuracy of action recognition. In this paper, we propose improved motion scale-inviriant feature transform （iMoSIFT） algorithm to eliminate the negative effects caused by camera motions. Based on iMoSIFT, we consider the spatial-temporal structure relationship among iMoSIFT interest points, and adopt locally weighted word context descriptors to code this relationship. Then, we use two-layer BOW representation for every video clip. The proposed approach is evaluated on available datasets, namely Weizemann, KTH and UCF sports. The experimental results clearly demonstrate the effectiveness of the proposed approach.展开更多
The proliferation of forums and blogs leads to challenges and opportunities for processing large amounts of information. The information shared on various topics often contains opinionated words which are qualitative ...The proliferation of forums and blogs leads to challenges and opportunities for processing large amounts of information. The information shared on various topics often contains opinionated words which are qualitative in nature. These qualitative words need statistical computations to convert them into useful quantitative data. This data should be processed properly since it expresses opinions. Each of these opinion bearing words differs based on the significant meaning it conveys. To process the linguistic meaning of words into data and to enhance opinion mining analysis, we propose a novel weighting scheme, referred to as inferred word weighting(IWW). IWW is computed based on the significance of the word in the document(SWD) and the significance of the word in the expression(SWE) to enhance their performance. The proposed weighting methods give an analytic view and provide appropriate weights to the words compared to existing methods. In addition to the new weighting methods, another type of checking is done on the performance of text classification by including stop-words. Generally, stop-words are removed in text processing. When this new concept of including stop-words is applied to the proposed and existing weighting methods, two facts are observed:(1) Classification performance is enhanced;(2) The outcome difference between inclusion and exclusion of stop-words is smaller in the proposed methods, and larger in existing methods. The inferences provided by these observations are discussed. Experimental results of the benchmark data sets show the potential enhancement in terms of classification accuracy.展开更多
基金Supported by the National Natural Science Foundation of China(61231015,61170023)National High Technology Research and Development Program of China(863 Program,2015AA016306)+3 种基金Internet of Things Development Funding Project of Ministry of Industry in 2013(25)Technology Research Program of Ministry of Public Security(2014JSYJA016)Major Science and Technology Innovation Plan of Hubei Province(2013AAA020)Natural Science Foundation of Hubei Province(2014CFB712)
文摘Classic local space-time features are successful representations for action recognition in videos. However, these features always confuse object motions with camera motions, which seriously affect the accuracy of action recognition. In this paper, we propose improved motion scale-inviriant feature transform （iMoSIFT） algorithm to eliminate the negative effects caused by camera motions. Based on iMoSIFT, we consider the spatial-temporal structure relationship among iMoSIFT interest points, and adopt locally weighted word context descriptors to code this relationship. Then, we use two-layer BOW representation for every video clip. The proposed approach is evaluated on available datasets, namely Weizemann, KTH and UCF sports. The experimental results clearly demonstrate the effectiveness of the proposed approach.
文摘The proliferation of forums and blogs leads to challenges and opportunities for processing large amounts of information. The information shared on various topics often contains opinionated words which are qualitative in nature. These qualitative words need statistical computations to convert them into useful quantitative data. This data should be processed properly since it expresses opinions. Each of these opinion bearing words differs based on the significant meaning it conveys. To process the linguistic meaning of words into data and to enhance opinion mining analysis, we propose a novel weighting scheme, referred to as inferred word weighting(IWW). IWW is computed based on the significance of the word in the document(SWD) and the significance of the word in the expression(SWE) to enhance their performance. The proposed weighting methods give an analytic view and provide appropriate weights to the words compared to existing methods. In addition to the new weighting methods, another type of checking is done on the performance of text classification by including stop-words. Generally, stop-words are removed in text processing. When this new concept of including stop-words is applied to the proposed and existing weighting methods, two facts are observed:(1) Classification performance is enhanced;(2) The outcome difference between inclusion and exclusion of stop-words is smaller in the proposed methods, and larger in existing methods. The inferences provided by these observations are discussed. Experimental results of the benchmark data sets show the potential enhancement in terms of classification accuracy.
