视频检索中基于GMM聚类的无监督情感场景检测

为了高效地从视频中检索出激动人心的场面,提出了一种基于高斯混合模型的无监督情感场景检测方法.首先,从面部选取42个特征点,并定义10种面部特征;然后,利用高斯混合模型将视频的帧划分为多个聚类;最后,利用每一帧的面部表情分类结果将情感场景划分为单个聚类,并通过场景集成和删除完成检测.在生活记录视频和MMI人脸表情数据库上的实验结果表明,该方法的检测率、分类率分别高达98％,95％,检测5分钟左右的情感场景视频仅需0.138 s,性能优于几种较为先进的检测方法.     

面向车载监控视频的快速检索设计

在车载监控过程中,为提高视频检索的效率,提出了一种快速检索方法.首先,采用一种改进的基于块匹配的方法快速消除视频抖动,完成图像序列预处理;然后根据不同用户需求,通过自动和主动两种方式检索视频.实验结果表明,该方法在不遗漏重要画面的同时,能够快速检索到用户感兴趣的视频片段,为用户减少了大量的视频浏览时间.     

基于Map/Reduce的海量视频图像检索系统设计

【】针对齐齐哈尔市公安视频监控系统中每天所产生的大量视频数据,这对视频图像的检索、管理及安全产生了迫切的需求,视频图像的检索存在两个急需解决的问题,一个是视频检索的准确度的问题,另一个是检索效率的问题。面对海量的视频数据库,本文提出了基于Map/Reduce分布式计算模型与关键帧算法的结合。既提高检索效率,又提高了检索的准确率。     

面向检索的压缩域空时视频目标检测与分割

面向语义视频检索,提出一种压缩域的目标分割新算法。它直接基于压缩码流中运动矢量和DCT系数,经过运动检测、矢量分水岭分割、目标融合与修正、后处理与跟踪等步骤提取空时视频目标。整个过程主要基于压缩域进行,无需视频码流的完全解码。对不同测试序列的实验测试结果显示算法能基于压缩域提取较为精确的空时视频目标,并具有较好的鲁棒性。     

Local maximum edge binary patterns: A new descriptor for image retrieval and object tracking

A new algorithm meant for content based image retrieval (CBIR) and object tracking applications is presented in this paper. The local region of image is represented by local maximum edge binary patterns (LMEBP), which are evaluated by taking into consideration the magnitude of local difference between the center pixel and its neighbors. This LMEBP differs from the existing LBP in a manner that it extracts the information based on distribution of edges in an image. Further, the effectiveness of our algorithm is confirmed by combining it with Gabor transform. Four experiments have been carried out for proving the worth of our algorithm. Out of which three are meant for CBIR and one for object tracking. It is further mentioned that the database considered for first three experiments are Brodatz texture database (DB1), MIT VisTex database (DB2), rotated Brodatz database (DB3) and the fourth contains three observations. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to LBP and other existing transform domain techniques.     

Adaptive deep feature aggregation using Fourier transform and low-pass filtering for robust object retrieval

With the rapid development of deep learning techniques, convolutional neural networks (CNN) have been widely investigated for the feature representations in the image retrieval task. However, the key step in CNN-based retrieval, i.e., feature aggregation has not been solved in a robust and general manner when tackling different kinds of images. In this paper, we present a deep feature aggregation method for image retrieval using the Fourier transform and low-pass filtering, which can adaptively compute the weights for each feature map with discrimination. Specifically, the low-pass filtering can preserve the semantic information in each feature map by transforming images to the frequency domain. In addition, we develop three adaptive methods to further improve the robustness of feature aggregation, i.e., Region of Interests (ROI) selection, spatial weighting and channel weighting. Experimental results demonstrate the superiority of the proposed method in comparison with other state-of-the-art, in achieving robust and accurate object retrieval under five benchmark datasets.     

Rapid detection of camera tampering and abnormal disturbance for video surveillance system

Camera tampering may indicate that a criminal act is occurring. Common examples of camera tampering are turning the camera lens to point to a different direction (i.e., camera motion) and covering the lens by opaque objects or with paint (i.e., camera occlusion). Moreover, various abnormalities such as screen shaking, fogging, defocus, color cast, and screen flickering can strongly deteriorate the performance of a video surveillance system. This study proposes an automated method for rapidly detecting camera tampering and various abnormalities for a video surveillance system. The proposed method is based on the analyses of brightness, edge details, histogram distribution, and high-frequency information, making it computationally efficient. The proposed system runs at a frame rate of 20–30 frames/s, meeting the requirement of real-time operation. Experimental results show the superiority of the proposed method with an average of 4.4% of missed events compared to existing works.     

视频检索中的边界检测算法

从视频检索技术的发展背景出发，重点介绍了视频检索技术中镜头的边界检测技术。分别介绍了基于解压的全图像序列的算法，基于压缩视频的算法以及基于确定变换模型的算法三类算法。最后，对基于内容的视频检索提出一些值得进一步研究的问题。     

Efficient data retrieval algorithms for multiple requests in MIMO wireless networks

Given a set of multiple channels, a set of multiple requests, where each request contains multiple requested data items and a client equipped with multiple antennae, the multi‐antenna‐based multirequest data retrieval problem (DRMR‐MA) is to find a data retrieval sequence for downloading all data items of the requests allocated to each antenna, such that the maximum access latency of all antennae is minimized. Most existing approaches for the data retrieval problem focus on either single antenna or single request and are hence not directly applicable to DRMR‐MA for retrieving multiple requests. This paper proposes two data retrieval algorithms that adopt two different grouping schemes to solve DRMR‐MA so that the requests can be suitably allocated to each antenna. To find the data retrieval sequence of each request efficiently, we present a data retrieval scheme that converts a wireless data broadcast system to a special tree. Experimental results show that the proposed scheme is more efficient than other existing schemes. Copyright © 2014 John Wiley & Sons, Ltd.     

Automatic segmentation of video object planes in MPEG-4 based on spatio-temporal information

Segmentation of semantic Video Object Planes (VOP‘s) from video sequence is a key to the standard MPEG-4 with content-based video coding. In this paper, the approach of automatic Segmentation of VOP‘s Based on Spatio-Temporal Information (SBSTI) is proposed. The proceeding results demonstrate the good performance of the algorithm.     

PTR-CNN for in-loop filtering in video coding

A deep learning method called PTR-CNN (Predicted frame with Transform unit partition and prediction Residual aided CNN) is proposed for in-loop filtering in video compression. To reduce the computational complexity of an end-to-end CNN in-loop filter, a non-learning method of reference frame selection is designed to select the highest quality frame based on the frame’s blurriness and smoothiness scores. The transform unit (TU) partition and the prediction residual (PR) of the current frame are used as extra inputs to the neural network as the filtering guidance. The selected similar and high quality reference frame (RF) and the current unfiltered frame (CUF) are input to a CNN based motion compensation module to generate a predicted frame (PF). Finally input the PF, the CUF, the CUF’s TU partition and the CUF’s PR into the main CNN to reconstruct the filtered frame. The model is implemented in Tensorflow and tested in HEVC and AV1. Experimental results show that the complexity of proposed PTR-CNN is less than SOTA CNN-based reference aided in-loop filtering methods and slightly outperforms their RD performance. The scheme introduces a complexity overhead of 7% on the encoder. In particular, for random access, the proposed model achieves 11.78% coding gain over HEVC with DBF/SAO off, while has a gain of 4.76% over HEVC with DBF/SAO on. Ablation study demonstrates that the RF contributes about 10% of the total gain, and the TU and PR contribute over 4% of the total one, proving the effectiveness of each module. Moreover, it is observed that the proposed method can restore detailed structures and textures and hence improve the subjective quality.     

Structured representations in a content based image retrieval context

Here, we propose an automatic system to annotate and retrieve images. We assume that regions in an image can be described using a vocabulary of blobs. Blobs are generated from image features using clustering. Features are locally extracted on regions to capture Color, Texture and Shape information. Regions are processed by an efficient segmentation algorithm. Images are structured into a region adjacency graph to consider spatial relationships between regions. This representation is used to perform a similarity search into an image set. Hence, the user can express his need by giving a query image, and thereafter receiving as a result all similar images. Our graph based approach is benchmarked to conventional Bag of Words methods. Results tend to reveal a good behavior in classification of our graph based solution on two publicly available databases. Experiments illustrate that a structural approach requires a smaller vocabulary size to reach its best performance.     

MPEG-4视频验证模型中基于内容的编解码算法

ＭＰＥＧ－４是一个正在制定的编码标准,除了具有 ＭＰＥＧ－１和 ＭＰＥＧ－２标准的基于“帧”的功能以外,ＭＰＥＧ－４视频编码算法还支持多媒体环境中对视频景物内的“物体”进行存取与操纵。文中描述了第四版ＭＰＥＧ－４视频验证模型的结构及其提供的主要编码工具和算法。     

Three Degree Binary Graph and Shortest Edge Clustering for re-ranking in multi-feature image retrieval

Graph methods have been widely employed in re-ranking for image retrieval. Although we can effectively find visually similar images through these methods, the ranking lists given by those approaches may contain some candidates which appear to be irrelevant to a query. Most of these candidates fall into two categories: (1) the irrelevant outliers located near to the query images in a graph; and (2) the images from another cluster which close to the query. Therefore, eliminating these two types of images from the ordered retrieval sets is expected to further boost the retrieval precision. In this paper, we build a Three Degree Binary Graph (TDBG) to eliminate the outliers and utilize a set-based greedy algorithm to reduce the influence of adjacent manifolds. Moreover, a multi-feature fusion method is proposed to enhance the retrieval performance further. Experimental results obtained on three public datasets demonstrate the superiority of the proposed approach.     

语义网在网络检索中的发展趋势

通过介绍语义网的相关概念、特点，分析语义网如何能够在网络检索中完成令人满意的精确、智能检索．并对语义网所面临的问题和发展前景进行描述。     

光电跟踪测量系统多传感器融合跟踪设计与实现

本文从多传感器结构设计、融合跟踪算法两方面,进行了光电跟踪测量系统多传感器融合跟踪的设计与实现方法研究。设计了一套集可见光测量、红外测量和激光测量为一体的光电跟踪测量系统,实现了适应不同环境背景下的单站定位测量功能。     

Unsupervised image segmentation based on analysis of binary partition tree for salient object extraction

This paper proposes an unsupervised image segmentation approach aimed at salient object extraction. Starting from an over-segmentation result of a color image, region merging is performed using a novel dissimilarity measure considering the impact of color difference, area factor and adjacency degree, and a binary partition tree (BPT) is generated to record the whole merging sequence. Then based on a systematic analysis of the evaluated BPT, an appropriate subset of nodes is selected from the BPT to represent a meaningful segmentation result with a small number of segmented regions. Experimental results demonstrate that the proposed approach can obtain a better segmentation performance from the perspective of salient object extraction.     

TV运动画面下的OSD显示优化

对运动估计和运动补偿的概念进行了简单的介绍,对电视机在运用运动估计和运动补偿技术后运动画面下OSD显示存在的问题进行了论述,并分析解决运动画面下OSD显示异常的方法,重点阐述了OSD信号和视频信号分开传输的优化OSD显示的方法。