XJTU at TRECVID2008 High-Level Feature Extraction

BBC rush summarization and High-Level Feature extraction In TRECVID2008

In this paper, first we describe rushes summarization system which is made this year for the TRECVID2008 BBC rushes task. Our aim this year is to build up base system using minimum information and to see how it works. In the second we will describe about our High-level feature extraction system briefly.

Shanghai Jiao Tong University participation in high-level feature extraction, automatic search and surveillance event detectionat TRECVID 2008

In this paper, we describe our participation for high-level feature extraction, automatic search and surveillance event detection at TRECVID 2008 evaluation. In high-level feature extraction, we use selective attention model to extract visual salient feature which highlights the most visual attractive information of an image. Besides this, we extract 7 low-level features for various modalities ...

REGIM at TRECVID2008: High-level Features Extraction and Video Search

Columbia University/VIREO-CityU/IRIT TRECVID2008 High-Level Feature Extraction and Interactive Video Search

! A_CU-run6: local feature alone – average fusion of 3 SVM classification results for each concept using various feature representation choices. ! A_CU-run5: linear weighted fusion of A_CU-run6 with two grid-based global features (color moment and wavelet texture). ! A_CU-run4: linear weighted fusion of A_CU-run5 with a SVM classification result using detection scores of CU-VIREO374 as features...

ISM TRECVID2008 High-level Feature Extraction

We studied two methods for the high-level feature extraction (HLF) task: (1) a method based on support vector machines (SVMs) with walk-based graph kernels [1], and (2) a method based on the prefixspan boosting (pboost) algorithm [2]. In the former method, each image is first segmented into a finite set of homogeneous segments and then represented as a segmentation graph where each vertex is a ...