近似重复视频检索方法研究

随着互联网的飞速发展,越来越多的视频被上传和下载,然而这些海量的视频中有很大的比例是近似重复的,这些近似重复的视频会给版权控制、视频检索准确性等造成一定影响,同时也会增加运营商的存储和处理成本。如何在大规模的视频集中找出近似重复的视频变得日益重要。本文对近几年关于近似重复视频检索方面的相关工作和研究成果进行了深入调研,详细论述了当前近似视频检索技术的现状及关键技术,并对其发展进行了展望。     

一种基于图的近重复视频子序列匹配算法

为了解决近重复视频检测中的效果和效率问题, 提出了一种基于图的近重复视频子序列匹配算法。将基于关键帧特征的相似性查询结果构建成匹配结果图, 进而将近重复视频检测转换成一个在匹配结果图中查找最长路径的问题。该算法有三个主要优势：a）它能在众多杂乱的匹配结果中找到最佳的匹配序列, 有效剔除了某些假“高相似度”匹配带来的噪声, 因而能在一定程度上弥补底层特征描述力的不足; b）由于它充分考虑和利用了视频序列的时序特性, 具有很高的近重复视频定位准确度; c）它能自动检测出匹配结果图中存在的多条离散路径, 从而能一次性检测出两段视频中可能存在多段近重复视频的情形。提出的算法不仅提高了检测的准确度, 而且提高了检测效率, 取得了良好的实践效果。     

Advance on large scale near-duplicate video retrieval

Emerging Internet services and applications attract increasing users to involve in diverse video-related activities, such as video searching, video downloading, video sharing and so on. As normal operations, they lead to an explosive growth of online video volume, and inevitably give rise to the massive near-duplicate contents. Near-duplicate video retrieval (NDVR) has always been a hot topic. The primary purpose of this paper is to present a comprehensive survey and an updated reviewof the advance on large-scaleNDVR to supply guidance for researchers. Specifically, we summarize and compare the definitions of near-duplicate videos (NDVs) in the literature, analyze the relationship between NDVR and its related research topics theoretically, describe its generic framework in detail, investigate the existing state-of-the-art NDVR systems. Finally, we present the development trends and research directions of this topic.     

MapReduce-based clustering for near-duplicate image identification

In this paper, an effective algorithm is developed for tackling the problem of near-duplicate image identification from large-scale image sets, where the LLC (locality-constrained linear coding) method is seamlessly integrated with the maxIDF cut model to achieve more discriminative representations of images. By incorporating MapReduce framework for image clustering and pairwise merging, the near duplicates of images can be identified effectively from large-scale image sets. An intuitive strategy is also introduced to guide the process for parameter selection. Our experimental results on large-scale image sets have revealed that our algorithm can achieve significant improvement on both the accuracy rates and the computation efficiency as compared with other baseline methods.     

Multiscale video sequence matching for near-duplicate detection and retrieval

As one of key technologies in content-based near-duplicate detection and video retrieval, video sequence matching can be used to judge whether two videos exist duplicate or near-duplicate segments or not. Despite a lot of research efforts devoted in recent years, how to precisely and efficiently perform sequence matching among videos (which may be subject to complex audio-visual transformations) from a large-scale database still remains a pretty challenging task. To address this problem, this paper proposes a multiscale video sequence matching (MS-VSM) method, which can gradually detect and locate the similar segments between videos from coarse to fine scales. At the coarse scale, it makes use of the Maximum Weight Matching (MWM) algorithm to rapidly select several candidate reference videos from the database for a given query. Then for each candidate video, its most similar segment with respect to the given query is obtained at the middle scale by the Constrained Longest Ascending Matching Subsequence (CLAMS) algorithm, and then can be used to judge whether that candidate exists near-duplicate or not. If so, the precise locations of the near-duplicate segments in both query and reference videos are determined at the fine scale by using bi-directional scanning to check the matching similarity at the segments’ boundaries. As such, the MS-VSM method can achieve excellent near-duplicate detection accuracy and localization precision with a very high processing efficiency. Extensive experiments show that it outperforms several state-of-the-art methods remarkably on several benchmarks.

     

Concept-based near-duplicate video clip detection for novelty re-ranking of web video search results

State-of-the-art near-duplicate video clip (NDVC) detection for novelty re-ranking uses non-semantic low-level features (color/texture) to detect and eliminate “content-based NDVC” and increases content level novelty in the top results. However, humans may perceive a video as near duplicate from a semantic perspective as well. In this paper, we propose concept-based near-duplicate video clip (CBNDVC) detection technique for novelty re-ranking. We identify “semantic NDVC”, making use of the semantic features (events/concepts) and re-rank the top results to increase the content as well as semantic novelty. Videos are represented as a multivariate time series of confidence values of relevant concepts and thereafter discovery of CBNDVC clusters is achieved by conceptual clustering. Obtained results show higher precision and recall from the user’s perspective.     

Visual word proximity and linguistics for semantic video indexing and near-duplicate retrieval

Bag-of-visual-words (BoW) has recently become a popular representation to describe video and image content. Most existing approaches, nevertheless, neglect inter-word relatedness and measure similarity by bin-to-bin comparison of visual words in histograms. In this paper, we explore the linguistic and ontological aspects of visual words for video analysis. Two approaches, soft-weighting and constraint-based earth mover’s distance (CEMD), are proposed to model different aspects of visual word linguistics and proximity. In soft-weighting, visual words are cleverly weighted such that the linguistic meaning of words is taken into account for bin-to-bin histogram comparison. In CEMD, a cross-bin matching algorithm is formulated such that the ground distance measure considers the linguistic similarity of words. In particular, a BoW ontology which hierarchically specifies the hyponym relationship of words is constructed to assist the reasoning. We demonstrate soft-weighting and CEMD on two tasks: video semantic indexing and near-duplicate keyframe retrieval. Experimental results indicate that soft-weighting is superior to other popular weighting schemes such as term frequency (TF) weighting in large-scale video database. In addition, CEMD shows excellent performance compared to cosine similarity in near-duplicate retrieval.     

Projection-detecting filter for video cut detection

This paper discusses a video cut detection method. Cut detection is an important technique for making videos easier to handle. First, this paper analyzes the distribution of the image differenceV to clarify the characteristics that makeV suitable for cut detection. We propose a cut detection method that uses a projection (an isolated sharp peak) detecting filter. A motion sensitiveV is used to stabilizeV projections at cuts, and cuts are detected more reliably with this filter. The method can achieve high detection rates without increasing the rate of misdetection. Experimental results confirm the effectiveness of the filter.     

A Bayesian approach to joint tracking and identification of geometric shapes in video sequences

A Bayesian approach is proposed for joint tracking and identification. These two problems are often addressed independently in the literature, leading to suboptimal performance. In a Bayesian approach, a prior distribution is set on both the hypothesis space and the associated parameter space. Although this is straightforward from a conceptual viewpoint, it is typically impossible to perform inference in closed-form. We discuss an advanced particle filtering approach to solve this computational problem and apply this algorithm to joint tracking and identification of geometric forms in video sequences.