In object detection, detecting an object with 100 pixels is substantially different from detecting an object with 10 pixels. Many object detection algorithms assume that the pedestrian scale is fixed during detection,...In object detection, detecting an object with 100 pixels is substantially different from detecting an object with 10 pixels. Many object detection algorithms assume that the pedestrian scale is fixed during detection, such as the DPM detector. However, detectors often give rise to different detection effects under the circumstance of different scales. If a detector is used to perform pedestrian detection in different scales, the accuracy of pedestrian detection could be improved. A multi-resolution DPM pedestrian detection algorithm is proposed in this paper. During the stage of model training, a resolution factor is added to a set of hidden variables of a latent SVM model. Then, in the stage of detection, a standard DPM model is used for the high resolution objects and a rigid template is adopted in case of the low resolution objects. In our experiments, we find that in case of low resolution objects the detection accuracy of a standard DPM model is lower than that of a rigid template. In Caltech, the omission ratio of a multi-resolution DPM detector is 52% with 1 false positive per image (1FPPI);and the omission ratio rises to 59% (1FPPI) as far as a standard DPM detector is concerned. In the large-scale sample set of Caltech, the omission ratios given by the multi-resolution and the standard DPM detectors are 18% (1FPPI) and 26% (1FPPI), respectively.展开更多
Recently,the authors often see words such as youth slang,neologism and Internet slang on social networking sites(SNSs)that are not registered on dictionaries.Since the documents posted to SNSs include a lot of fresh i...Recently,the authors often see words such as youth slang,neologism and Internet slang on social networking sites(SNSs)that are not registered on dictionaries.Since the documents posted to SNSs include a lot of fresh information,they are thought to be useful for collecting information.It is important to analyse these words(hereinafter referred to as‘slang’)and capture their features for the improvement of the accuracy of automatic information collection.This study aims to analyse what features can be observed in slang by focusing on the topic.They construct topic models from document groups including target slang on Twitter by latent Dirichlet allocation.With the models,they chronologically the analyse change of topics during a certain period of time to find out the difference in the features between slang and general words.Then,they propose a slang classification method based on the change of features.展开更多
文摘In object detection, detecting an object with 100 pixels is substantially different from detecting an object with 10 pixels. Many object detection algorithms assume that the pedestrian scale is fixed during detection, such as the DPM detector. However, detectors often give rise to different detection effects under the circumstance of different scales. If a detector is used to perform pedestrian detection in different scales, the accuracy of pedestrian detection could be improved. A multi-resolution DPM pedestrian detection algorithm is proposed in this paper. During the stage of model training, a resolution factor is added to a set of hidden variables of a latent SVM model. Then, in the stage of detection, a standard DPM model is used for the high resolution objects and a rigid template is adopted in case of the low resolution objects. In our experiments, we find that in case of low resolution objects the detection accuracy of a standard DPM model is lower than that of a rigid template. In Caltech, the omission ratio of a multi-resolution DPM detector is 52% with 1 false positive per image (1FPPI);and the omission ratio rises to 59% (1FPPI) as far as a standard DPM detector is concerned. In the large-scale sample set of Caltech, the omission ratios given by the multi-resolution and the standard DPM detectors are 18% (1FPPI) and 26% (1FPPI), respectively.
文摘Recently,the authors often see words such as youth slang,neologism and Internet slang on social networking sites(SNSs)that are not registered on dictionaries.Since the documents posted to SNSs include a lot of fresh information,they are thought to be useful for collecting information.It is important to analyse these words(hereinafter referred to as‘slang’)and capture their features for the improvement of the accuracy of automatic information collection.This study aims to analyse what features can be observed in slang by focusing on the topic.They construct topic models from document groups including target slang on Twitter by latent Dirichlet allocation.With the models,they chronologically the analyse change of topics during a certain period of time to find out the difference in the features between slang and general words.Then,they propose a slang classification method based on the change of features.
