主题：Local Difference Binary for Ultra-fast and Distinctive Feature Description
主题：Diverse Sequential Subset Selection for Supervised Video Summarization 主讲人： 宫博庆（University of Southern California）
活动时间：2014年12月17日星期三（北京时间21:30）
文献阅读： - Xin Yang and Tim Cheng, Local Difference Binary for Ultra-fast and Distinctive Feature Description, IEEE TPAMI, 2014. [pdf]
- Xin Yang, Chong Huang and Tim Cheng, libLDB: A Library for Extracting Ultrafast and Distinctive Binary Feature Description, ACM International Conference on Multimedia (MM), Open Source Software Competition, 2014. [Project page]
- B. Gong, W. Chao, K. Grauman, and F. Sha. Diverse Sequential Subset Selection for Supervised Video Summarization. NIPS 2014. [pdf]
- Boqing Gong, Wei-lun Chao, Kristen Grauman, and Fei Sha. Large-Margin Determinantal Point Processes, arXiv1411.1537. [pdf]
讲座资料： - Diverse Sequential Subset Selection for Supervised Video Summarization. http://valser.org/webinar/slide/slides/20141217/Local-Difference-Binary-for-Ultrafast-and-Distinctive-Feature-Description.pdf 摘要: Supervised video summarization large-margin training method for DPP Video summarization is a challenging problem with great application potential. Whereas prior approaches, largely unsupervised in nature, focus on sampling useful frames and assembling them as summaries, we consider video summarization as a supervised subset selection problem. Our idea is to teach the system to learn from human-created summaries how to select informative and diverse subsets, so as to best meet evaluation metrics derived from human-perceived quality. To this end, we propose the sequential determinantal point process (seqDPP), a probabilistic model for diverse sequential subset selection. Our novel seqDPP heeds the inherent sequential structures in video data, thus overcoming the deficiency of the standard DPP, which treats video frames as randomly permutable items. Meanwhile, seqDPP retains the power of modeling diverse subsets, essential for summarization. Our extensive results of summarizing videos from 3 datasets demonstrate the superior performance of our method, compared to not only existing unsupervised methods but also naive applications of the standard DPP model.
- Local Difference Binary for Ultra-fast and Distinctive Feature Description. http://valser.org/webinar/slide/slides/20141217/Supervised%20Video%20Summarization%20-%20Boqing%20Gong.pptx 摘要：The efficiency, robustness and distinctiveness of a feature descriptor are critical to user experience and scalability of mobile computer vision apps, e.g. mobile augmented reality (AR). However, existing descriptors are either too computationally expensive to achieve real-time performance on a mobile devices such as smartphone or tablet, or not sufficiently robust and distinctive to identify correct matches from a large database. As a result, current mobile AR systems still only have limited capabilities, which greatly restrict their deployment in practice. In this talk, we present a highly efficient, robust and distinctiveness binary descriptor, called local difference binary (LDB). LDB directly computes a binary string from an image patch using simply intensity and gradient difference tests on pairwise grid cells within the patch. To select an optimized set of grid cell pairs, we densely sample grid cells from an image patch and then leverage a modified AdaBoost algorithm to automatically extract a small set of critical ones with the goal of maximizing the Hamming distance between mismatches while minimizing it between matches. Experimental results demonstrate that LDB is extremely fast to compute and to match a large database due to its high robustness and distinctiveness. Compared to the state-of-the-art binary descriptors, primarily designed for speed, LDB has similar efficiency for descriptor construction, while achieving a greater accuracy and faster matching speed when matching over a large database with 2.3M descriptors on mobile devices.
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