Lin Shouxun

Fast 4/spl times/4 intra-prediction mode selection for H.264

In this paper, we propose a fast intra-prediction mode selection method for JVT video coding standard H.264. Based on local edge information obtained by calculating edge feature parameters, the method can reduce the computational complexity considerably while maintaining similar PSNR and bit rate. Experimental results are presented to show the effectiveness of the proposed method

Adult Image Detection Combining BoVW Based on Region of Interest and Color Moments

To prevent pornography from spreading on the Internet effectively, we propose a novel method of adult image detection which combines bag-ofvisual-words (BoVW) based on region of interest (ROI) and color moments (CM). The goal of BoVW is to automatically mine the local patterns of adult contents, called visual words. The usual BoVW method clusters visual words from the patches in the whole image and adopts the weighting schemes of hard assignment. However, there are many background noises in the whole image and soft-weighting scheme is better than hard assignment. Therefore, we propose the method of BoVW based on ROI, which includes two perspectives. Firstly, we propose to create visual words in ROI for adult image detection. The representative power of visual words can be improved because the patches in ROI are more indicative to adult contents than those in the whole image. Secondly, soft-weighting scheme is adopted to detect adult images. Moreover, CM is selected by evaluating some commonly-used global features to be combined with BoVW based on ROI. The experiments and the comparison with the stateof-the-art methods show that our method is able to remarkably improve the performance of adult image detection.To prevent pornography from spreading on the Internet effectively, we propose a novel method of adult image detection which combines bag-of-visual-words (BoVW) based on region of interest (ROI) and color moments (CM). The goal of BoVW is to automatically mine the local patterns of adult contents, called visual words. The usual BoVW method clusters visual words from the patches in the whole image and adopts the weighting schemes of hard assignment. However, there are many background noises in the whole image and soft-weighting scheme is better than hard assignment. Therefore, we propose the method of BoVW based on ROI, which includes two perspectives. Firstly, we propose to create visual words in ROI for adult image detection. The representative power of visual words can be improved because the patches in ROI are more indicative to adult contents than those in the whole image. Secondly, soft-weighting scheme is adopted to detect adult images. Moreover, CM is selected by evaluating some commonly-used global features to be combined with BoVW based on ROI. The experiments and the comparison with the state-of-the-art methods show that our method is able to remarkably improve the performance of adult image detection.

HTRDP evaluations on Chinese information processing and intelligent human-machine interface

From 1991 to 2005, China’s High Technology Research and Development Program (HTRDP) sponsored a series of technology evaluations on Chinese information processing and intelligent human-machine interface, which is called HTRDP evaluations, or “863” evaluations in brief. This paper introduces the HTRDP evaluations in detail. The general information of the HTRDP evaluation is presented first, including the history, the concerned technology categories, the organizer, the participants, and the procedure, etc. Then the evaluations on each technology are described in detail respectively, covering Chinese word segmentation, machine translation, acoustic speech recognition, text to speech, text summarization, text categorization, information retrieval, character recognition, and face detection and recognition. For the evaluations on each technology categories, the history, the evaluation tasks, the data, the evaluation method, etc., are given. The last section concludes the paper and discusses possible future work.