Peking University at TRECVID 2008: High Level Feature Extraction
نویسندگان
چکیده
We participated in one task of TRECVID 2008, that is, the high-level feature extraction (HLFE). This paper presents our approaches and results on the HLFE task. We mainly focus on exploring the data imbalance learning in this year, and propose two methods for this problem: (1) adaptive borderline-SMOTE and under-sampling SVM (ABUSVM), and (2) concept category. Our approach can be divided into two phases: feature representation and data imbalance learning. In feature representation phase, four low-level visual features namely color moment grid (CMG), local binary pattern (LBP), Gabor wavelet texture (Gabor), and edge histogram layout (EHL) are combined together in an “early fusion” manner. In data imbalance learning phase, ABUSVM and concept category are employed jointly to handle the data imbalance problem. In addition, we also investigate the fusion of 2005 and 2008 training data to improve the performance. The experimental results show our four visual features, ABUSVM, and concept category are effective to improve the performance, while the fusion of 2005 training data decreases the result.
منابع مشابه
Bilkent University Multimedia Database Group at TRECVID 2008
Bilkent University Multimedia Database Group (BILMDG) participated in two tasks at TRECVID 2008: content-based copy detection (CBCD) and high-level feature extraction (FE). Mostly MPEG-7 [1] visual features, which are also used as low-level features in our MPEG-7 compliant video database management system, are extracted for these tasks. This paper discusses our approaches in each task.
متن کاملIRIM at TRECVID 2008: High Level Feature Extraction
The IRIM group is a consortium of French teams working on Multimedia Indexing and Retrieval. This paper describes our participation to the TRECVID 2008 High Level Features detection task. We evaluated several fusion strategies and especially rank fusion. Results show that including as many low-level and intermediate features as possible is the best strategy, that SIFT features are very importan...
متن کاملXJTU at TRECVID2008 High-Level Feature Extraction
In this paper, we present our experiments in TRECVID 2008 about High-Level feature extraction task. This is the first year for our participation in TRECVID, our system adopts some popular approaches that other workgroups proposed before. We proposed 2 advanced low-level features NEW Gabor texture descriptor and the Compact-SIFT Codeword histogram. Our system applied well-known LIBSVM to train t...
متن کاملMSRA atT TRECVID 2008: High-Level Feature Extraction and Automatic Search
This paper describes the MSRA experiments for TRECVID 2008. We performed the experiments in high-level feature extraction and automatic search tasks. For high-level feature extraction, we representatively investigated the benefit of global and local low-level features by a variety of learning-based methods, including supervised and semi-supervised learning algorithms. For automatic search, we f...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2008