Strategies for Searching Video Content with Text Queries or Video Examples

The large number of user-generated videos uploaded on to the Internet everyday has led to many commercial video search engines, which mainly rely on text metadata for search. However, metadata is often lacking for user-generated videos, thus these videos are unsearchable by current search engines. Therefore, content-based video retrieval (CBVR) tackles this metadata-scarcity problem by directly analyzing the visual and audio streams of each video. CBVR encompasses multiple research topics, including low-level feature design, feature fusion, semantic detector training and video search/reranking. We present novel strategies in these topics to enhance CBVR in both accuracy and speed under different query inputs, including pure textual queries and query by video examples. Our proposed strategies have been incorporated into our submission for the TRECVID 2014 Multimedia Event Detection evaluation, where our system outperformed other submissions in both text queries and video example queries, thus demonstrating the effectiveness of our proposed approaches

Sparse Transfer Learning for Interactive Video Search Reranking

Visual reranking is effective to improve the performance of the text-based video search. However, existing reranking algorithms can only achieve limited improvement because of the well-known semantic gap between low level visual features and high level semantic concepts. In this paper, we adopt interactive video search reranking to bridge the semantic gap by introducing user's labeling effort. We propose a novel dimension reduction tool, termed sparse transfer learning (STL), to effectively and efficiently encode user's labeling information. STL is particularly designed for interactive video search reranking. Technically, it a) considers the pair-wise discriminative information to maximally separate labeled query relevant samples from labeled query irrelevant ones, b) achieves a sparse representation for the subspace to encodes user's intention by applying the elastic net penalty, and c) propagates user's labeling information from labeled samples to unlabeled samples by using the data distribution knowledge. We conducted extensive experiments on the TRECVID 2005, 2006 and 2007 benchmark datasets and compared STL with popular dimension reduction algorithms. We report superior performance by using the proposed STL based interactive video search reranking.Comment: 17 page

The THUMOS Challenge on Action Recognition for Videos "in the Wild"

Automatically recognizing and localizing wide ranges of human actions has crucial importance for video understanding. Towards this goal, the THUMOS challenge was introduced in 2013 to serve as a benchmark for action recognition. Until then, video action recognition, including THUMOS challenge, had focused primarily on the classification of pre-segmented (i.e., trimmed) videos, which is an artificial task. In THUMOS 2014, we elevated action recognition to a more practical level by introducing temporally untrimmed videos. These also include `background videos' which share similar scenes and backgrounds as action videos, but are devoid of the specific actions. The three editions of the challenge organized in 2013--2015 have made THUMOS a common benchmark for action classification and detection and the annual challenge is widely attended by teams from around the world. In this paper we describe the THUMOS benchmark in detail and give an overview of data collection and annotation procedures. We present the evaluation protocols used to quantify results in the two THUMOS tasks of action classification and temporal detection. We also present results of submissions to the THUMOS 2015 challenge and review the participating approaches. Additionally, we include a comprehensive empirical study evaluating the differences in action recognition between trimmed and untrimmed videos, and how well methods trained on trimmed videos generalize to untrimmed videos. We conclude by proposing several directions and improvements for future THUMOS challenges.Comment: Preprint submitted to Computer Vision and Image Understandin

TRECVID 2009 - goals, tasks, data, evaluation mechanisms and metrics

The TREC Video Retrieval Evaluation (TRECVID) 2009 was a TREC-style video analysis and retrieval evaluation, the goal of which was to promote progress in content-based exploitation of digital video via open, metrics-based evaluation. Over the last 9 years TRECVID has yielded a better understanding of how systems can effectively accomplish such processing and how one can reliably benchmark their performance. 63 teams from various research organizations — 28 from Europe, 24 from Asia, 10 from North America, and 1 from Africa — completed one or more of four tasks: high-level feature extraction, search (fully automatic, manually assisted, or interactive), copy detection, or surveillance event detection. This paper gives an overview of the tasks, data used, evaluation mechanisms and performanc

A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection

This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter

An Efficient Perceptual of Content Based Image Retrieval System Using SVM and Evolutionary Algorithms

The CBIR tends to index and retrieve images based on their visual content. CBIR avoids several issues related to traditional ways that of retrieving images by keywords. Thus, a growing interest within the area of CBIR has been established in recent years. The performance of a CBIR system mainly depends on the particular image representation and similarity matching operate utilized. The CBIR tends to index and retrieve images supported their visual content. CBIR avoids several issues related to traditional ways that of retrieving images by keywords. Thus, a growing interest within the area of CBIR has been established in recent years. The performance of a CBIR system principally depends on the actual image illustration and similarity matching operate utilized. therefore a replacement CBIR system is projected which can give accurate results as compared to previously developed systems. This introduces the new composite framework for image classification in a content-based image retrieval system. The projected composite framework uses an evolutionary algorithm to select training samples for support vector machine (SVM). to style such a system, the most popular techniques of content-based image retrieval are reviewed initial. Our review reveals some limitations of the existing techniques, preventing them to accurately address some issues