Video Retrieval Techniques Research Articles

Prospects and Obstacles on the Path of Chinese’s Rise as a Global Language in the Context of the Internet

Abstract In this paper, the video retrieval technique based on model migration makes full use of the migration learning technique to build a text encoder and video encoder to obtain text and video features. The text is preprocessed based on the BERT model, and text features are extracted by combining TF-IDF and mutual information. The logistic regression algorithm is applied to achieve text classification. The text mining techniques above are used to parse YouTube Chinese learning resources to extract their dissemination features. The results show that 5,286 (61.48%) of YouTube Chinese learning resources are from the category of “education,” and 2,294 (26.68%) are from the category of “people and blogs.” The video resources category is the most popular and loved category among learners due to its highest average number of views (1238214.3). The dissemination is significantly impacted by teachers’ performance, vivid language, and interaction during the teaching process. The indirect effect value test between online interaction, technical support, and resource quality, as well as satisfaction and intention to continue using, met the criterion of significance (p<0.05). It indicates that perceived usefulness has a significant indirect effect (P<0.01) on the impact of knowledge dissemination. This study clarifies the dissemination influences that led to the rise of Chinese resources in the global context.

A content based image information retrieval and video thumbnail extraction framework using SOM

Searching an image or a video in a huge volume of graphical data is a tedious time-consuming process. If this search is performed using the conventional element matching technique, the complexity of the search will render the system useless. To overcome this problem, the current paper proposes a Content-Based Image Retrieval (CBIR) and a Content-Based Video Retrieval (CBVR) technique using clustering algorithms based on neural networks. Neural networks have proved to be quite powerful for dimensionality reduction due to their parallel computations. Retrieval of images in a large database on the basis of the content of the query image has been proved fast and efficient through practical results. Two images of the same object, but taken from different camera angles or have rotational and scaling transforms is also matched effectively. In medical domain, CBIR has proved to be a boon to the doctors. The tumor, cancer etc can be easily deducted comparing the images with normal to the images with diseases. Java and Weka have been used for implementation. The thumbnails extracted from the video facilitates the video search in a large videos database. The unsupervised nature of Self Organizing Maps (SOM) has made the software all the more robust.

Video retrieval using salient foreground region of motion vector based extracted keyframes and spatial pyramid matching

Despite enormous research efforts devoted by the research community to effectively and precisely perform video matching and retrieval among heterogeneous videos from large-scale video repositories still remains a complex and most challenging task. In order to address this complex challenge, a content based video retrieval technique is required, which can exploit the visual content of the videos for effective retrieval from the videos repositories. In our proposed method, we introduce a computer assisted video retrieval technique which can retrieve the visually similar videos stored in the repositories. To accomplish this task, video summarization based on motion vector is employed to select keyframes based on similar segments. To estimate the video content, salient foreground extraction is executed, and matching based on the spatial pyramid is employed for matching the keyframe features of query video with videos in the repositories. The contribution of the former process has two major sections for superior saliency map generation. Firstly, it heuristically integrates the regional property, contrast, and foreground descriptors together. Secondly, it introduces a new feature vector to characterize the foreground as an object descriptor, while the latter process is the extension of orderless bag-of-features representation, which has significant performance with respect to scene categorization. The video retrieval performance is compared with standard state-of-the-art techniques using real-time datasets. Experimental and usability studies provide satisfactory results for video retrieval based on evaluation metrics such as video sampling error, fidelity, precision, and recall.

Multimedia Image Processing Lab Experiment/Simulation

Image Processing in Multimedia Applications treats a number of critical topics in multimedia systems, with respect to image and video processing techniques and their implementations. These techniques include the Image and video compression techniques and standards, and Image and video indexing and retrieval techniques. Image Processing is an important tool to develop a Multimedia system design.

Comparative analysis on video retrieval technique using machine learning

Comparative analysis on video retrieval technique using machine learning

On Influential Trends in Interactive Video Retrieval: Video Browser Showdown 2015–2017

The last decade has seen innovations that make video recording, manipulation, storage, and sharing easier than ever before, thus impacting many areas of life. New video retrieval scenarios emerged as well, which challenge the state-of-the-art video retrieval approaches. Despite recent advances in content analysis, video retrieval can still benefit from involving the human user in the loop. We present our experience with a class of interactive video retrieval scenarios and our methodology to stimulate the evolution of new interactive video retrieval approaches. More specifically, the video browser showdown evaluation campaign is thoroughly analyzed, focusing on the years 2015–2017. Evaluation scenarios, objectives, and metrics are presented, complemented by the results of the annual evaluations. The results reveal promising interactive video retrieval techniques adopted by the most successful tools and confirm assumptions about the different complexity of various types of interactive retrieval scenarios. A comparison of the interactive retrieval tools with automatic approaches (including fully automatic and manual query formulation) participating in the TRECVID 2016 ad hoc video search task is discussed. Finally, based on the results of data analysis, a substantial revision of the evaluation methodology for the following years of the video browser showdown is provided.

Multimodal Features and Probability Extended Nearest Neighbor Classification for Content-Based Lecture Video Retrieval

AbstractDue to the ever-increasing number of digital lecture libraries and lecture video portals, the challenge of retrieving lecture videos has become a very significant and demanding task in recent years. Accordingly, the literature presents different techniques for video retrieval by considering video contents as well as signal data. Here, we propose a lecture video retrieval system using multimodal features and probability extended nearest neighbor (PENN) classification. There are two modalities utilized for feature extraction. One is textual information, which is determined from the lecture video using optical character recognition. The second modality utilized to preserve video content is local vector pattern. These two modal features are extracted, and the retrieval of videos is performed using the proposed PENN classifier, which is the extension of the extended nearest neighbor classifier, by considering the different weightages for the first-level and second-level neighbors. The performance of the proposed video retrieval is evaluated using precision, recall, andF-measure, which are computed by matching the retrieved videos and the manually classified videos. From the experimentation, we proved that the average precision of the proposed PENN+VQ is 78.3%, which is higher than that of the existing methods.

Review of Research on Shot Boundary Detection Algorithm of the Compressed Video Domain in Content-based Video Retrieval Technique

Based on the background of extensive use of the video retrieval technique in substation monitoring, this paper focuses on the research of the shot boundary detection methods in the video retrieval technique. The research content is mainly involved in the following four kinds of shot boundary detection methods: the shot boundary detection method based on the base tone, the shot boundary detection method based on the macro- block motion information, the shot boundary detection method based on the motion vector and the video shot segmentation method based on clustering. Through the in-depth explanation and comparison of four kinds of shot boundary detection methods, this paper systematically summarizes the characteristics of four kinds of shot boundary detection methods, and also vividly points out the problems of four kinds of shot boundary detection methods, thus pointing out the direction for the future development of video retrieval technique.

Animating pictures of water scenes using video retrieval

We present a system to quickly and easily create an animation of water scenes in a single image. Our method relies on a database of videos of water scenes and video retrieval technique. Given an input image, alpha masks specifying regions of interest, and sketches specifying flow directions, our system first retrieves appropriate video candidates from the database and create the candidate animations for each region of interest as the composite of the input image and the retrieved videos: this process spends less than one minute by taking advantage of parallel distributed processing. Our system then allows the user to interactively control the speed of the desired animation and select the appropriate animation. After selecting the animation for all the regions, the resulting animation is completed. Finally, the user optionally applies a texture synthesis algorithm to recover the appearance of the input image. We demonstrate that our system allows the user to create a variety of animations of water scenes.

An image-based near-duplicate video retrieval and localization using improved Edit distance

The rapid development of social network in recent years has spurred enormous growth of near-duplicate videos. The existence of huge volumes of near-duplicates shows a rising demand on effective near-duplicate video retrieval technique in copyright violation and search result reranking. In this paper, we propose an image-based algorithm using improved Edit distance for near-duplicate video retrieval and localization. By regarding video sequences as strings, Edit distance is used and extended to retrieve and localize near-duplicate videos. Firstly, bag-of-words (BOW) model is utilized to measure the frame similarities, which is robust to spatial transformations. Then, non-near-duplicate videos are filtered out by computing the proposed relative Edit distance similarity (REDS). Next, a detect-and-refine-strategy-based dynamic programming algorithm is proposed to generate the path matrix, which can be used to aggregate scores for video similarity measure and localize the similar parts. Experiments on CC_WEB_VIDEO and TREC CBCD 2011 datasets demonstrated the effectiveness and robustness of the proposed method in retrieval and localization tasks.

State of the Art: A Summary of Semantic Image and Video Retrieval Techniques

Our paper comprises of almost maximum used state of the art techniques in video retrieval as a brief summary. As there is developments in all fields, media becomes more popular and so people begun to search videos to know the world happenings visually. This paper gives a review of existed combinational methods, algorithms and techniques. The image retrieval was obtained from multiple lower ranking methods. The survey of many methods leads to an introduction of combination of a best method. The main focus of this survey is focused on video retrieval techniques to identify the underlying factors that affect the performance of the combinational method and to judge the effectiveness to provide efficient semantic video retrieval. Finally we descript a broad discussion about the advantages and drawbacks that have been in state of the art. Due to these reasons semantic video retrieval became a challenging issue in various industries.

A new visual navigation system for exploring biomedical Open Educational Resource (OER) videos.

Biomedical videos as open educational resources (OERs) are increasingly proliferating on the Internet. Unfortunately, seeking personally valuable content from among the vast corpus of quality yet diverse OER videos is nontrivial due to limitations of today's keyword- and content-based video retrieval techniques. To address this need, this study introduces a novel visual navigation system that facilitates users' information seeking from biomedical OER videos in mass quantity by interactively offering visual and textual navigational clues that are both semantically revealing and user-friendly. The authors collected and processed around 25 000 YouTube videos, which collectively last for a total length of about 4000 h, in the broad field of biomedical sciences for our experiment. For each video, its semantic clues are first extracted automatically through computationally analyzing audio and visual signals, as well as text either accompanying or embedded in the video. These extracted clues are subsequently stored in a metadata database and indexed by a high-performance text search engine. During the online retrieval stage, the system renders video search results as dynamic web pages using a JavaScript library that allows users to interactively and intuitively explore video content both efficiently and effectively.ResultsThe authors produced a prototype implementation of the proposed system, which is publicly accessible athttps://patentq.njit.edu/oer To examine the overall advantage of the proposed system for exploring biomedical OER videos, the authors further conducted a user study of a modest scale. The study results encouragingly demonstrate the functional effectiveness and user-friendliness of the new system for facilitating information seeking from and content exploration among massive biomedical OER videos. Using the proposed tool, users can efficiently and effectively find videos of interest, precisely locate video segments delivering personally valuable information, as well as intuitively and conveniently preview essential content of a single or a collection of videos.

Comprehensive Performance Comparison of Fourier, Walsh, Haar, Sine and Cosine Transforms for Video Retrieval with Partial Coefficients of Transformed Video

desire of better and faster retrieval techniques has always fuelled to the research in content based video retrieval (CBVR). The extended comparison of innovative content based video retrieval (CBVR) techniques based on feature vectors as partial coefficients of transformed video frames using various orthogonal transforms is presented in the paper. Here the popular transforms are considered like Cosine, Walsh, Haar, Sine, and Fourier transforms. The advantage of energy compaction of transforms in higher energy coefficients is taken to reduce the feature vector size per video by taking partial coefficients of transformed video frames. Reduced feature vector size results in less time for comparison of feature vectors resulting in faster retrieval of videos. The features are extracted in eight different ways from the transformed image. First all the coefficients of transformed image considered as 100% energy and then seven reduced coefficients sets are considered as feature vectors (as 99%, 98%, 97%, 96%, 95%, 90% and 85% energy of complete transformed video coefficients). To extract Gray feature sets the five video transforms are applied on gray image equivalents and the color components of videos. Then these seven reduced coefficients sets are used instead of using all coefficients of transformed videos as feature vector for video retrieval, resulting into better performance and lower computations. The video database of 500 video spread across 10 categories is used to test the performance of proposed CBVR techniques. 500 queries are fired on the database to find average accuracy values for all feature sets per transform for each proposed CBVR technique. The results have shown performance improvement (higher accuracy values) with partial coefficients compared to complete 100% energy of transformed of video frames at reduced computations resulting in faster retrieval. Haar transform surpasses all other considered transforms in performance with highest accuracy values with 90% of partial energy coefficients and size is lowered by 99.93% as compared to other transforms.

Low-complexity range tree for video synopsis system

This work proposes an efficient video retrieval technique for video synopsis. In a video system, the Region of Interest (ROI) should be extracted in a long video effectively such that users can browse it quickly and easily. Focusing on the characteristics of objects in the foreground of real-world video sequences, this work employs the Gaussian Mixture Model (GMM) and color-histograms for object detection. In order to reduce the search time, a new video synopsis search approach, a low-complexity range tree algorithm, is proposed to improve the effectiveness of searches for objects of interest matching pre-set conditions. With the time and space redundancy-reducing techniques of video synopsis, the objects of interest can be displayed within a short time. Objects and events can be found and displayed quickly without allocating time to watching non-ROIs. For the test video sequences, the results show an accuracy rate of 97 % and a processing speed of 32 FPS (frames per second) in the online phase, and the time complexity of object searching is reduced from O(N) to O(logD-1N).

Video Summarization: Survey on Event Detection and Summarization in Soccer Videos

In today's world, the rapid development of digital video and editing technology has led to fast growing of video data, creating the need for effective and advanced techniques for analysis and video retrieval, as multimedia repositories have made browsing, delivery of contents (video) and video retrieval very slow. Hence, video summarization proposes various ways for faster browsing among a large amount of data and also for content indexing. Many people spend their free time to watch or play different sports like soccer, cricket, etc. but it is not possible to watch each and every game due to the longer timing of the game. In such cases, the users may just want to view the summary of the video that is just an abstract of the original video, instead of watching the whole video that provides more information about the occurrence of various incidents in the video. It is preferable to watch just highlights of the game or just review/trailer of a movie. Apparently, summarizing a video is an important process. In this paper, video summarization approaches are discussed, that can generate static or dynamic summaries. We present different techniques for each mode in literature. We have discussed some features used for generating video summaries. As soccer is the world’s most famous game played and watched, it is taken as a case study. Research done in this domain is discussed. We conclude that there is a broad perspective for further research in this field.

An efficient access method for multimodal video retrieval

This paper presents the Slim 2 -tree, an efficient and effective content-based video retrieval technique allowing the use of multiple modalities within a single index structure. Slim 2 -tree is capable of dealing with different distance measures for the modalities and can perform both multimodal and unimodal searches using the same tree structure. Experimental studies on a large real dataset show the video similarity search performance of the proposed technique. Additionally, we present experiments comparing our method against state-of-the-art of multimodal solutions. Comparative test results demonstrate that our technique improves the performance of video similarity queries.

Multimedia Indexing and Retrieval Techniques: A Review

Retrieval of multimedia has become a requirement for many contemporary information systems. These systems need to provide browsing, querying, navigation, and, sometimes, composition capabilities involving various forms of media. In this survey, we review techniques for text, image, audio and video retrieval. We first look at indexing and retrieval techniques for text, audio, image and video. We also discuss features visual features for video retrieval such as colour, texture, shape. The indexing techniques are discussed for these features. We also compare most popular techniques used for indexing and retrieval.

Efficient targeted search using a focus and context video browser

Currently there are several interactive content-based video retrieval techniques and systems available. However, retrieval performance depends heavily on the means of interaction. We argue that effective CBVR requires efficient, specialized user interfaces. In this article we propose guidelines for such an interface, and we propose an effective CBVR engine: the ForkBrowser, which builds upon the principle of focus and context. This browser is evaluated using a combination of user simulation and real user evaluation. Results indicate that the ideas have merit, and that the browser performs very well when compared to the state-of-the-art in video retrieval.

Exploiting semantics on external resources to gather visual examples for video retrieval

With the huge and ever rising amount of video content available on the Web, there is a need to facilitate video retrieval functionalities on very large collections. Most of the current Web video retrieval systems rely on manual textual annotations to provide keyword-based search interfaces. These systems have to face the problems that users are often reticent to provide annotations, and that the quality of such annotations is questionable in many cases. An alternative commonly used approach is to ask the user for an image example, and exploit the low-level features of the image to find video content whose keyframes are similar to the image. In this case, the main limitation is the so-called semantic gap, which consists of the fact that low-level image features often do not match with the real semantics of the videos. Moreover, this approach may be a burden to the user, as it requires finding and providing the system with relevant visual examples. Aiming to address this limitation, in this paper, we present a hybrid video retrieval technique that automatically obtains visual examples by performing textual searches on external knowledge sources, such as DBpedia, Flickr and Google Images, which have different coverage and structure characteristics. Our approach exploits the semantics underlying the above knowledge sources to address the semantic gap problem. We have conducted evaluations to assess the quality of visual examples retrieved from the above external knowledge sources. The obtained results suggest that the use of external knowledge can provide valid visual examples based on a keyword-based query and, in the case that visual examples are provided explicitly by the user, it can provide visual examples that complement the manually provided ones to improve video search performance.

The Research on Video Retrieval Based on Content for Industrial Applications

Video retrieval has been widely used and broad prospects for development, is currently a hot research field of multimedia. From the analysis of content-based video retrieval system structure to fully explore the system structure involved in the construction of the video, shot cut detection, key frame extraction, construction scenes from the video stream or group of technology, feature extraction technology and video retrieval, browsing and retrieval feedback techniques, focused on analyzing some of the key technical advantages and disadvantages of various implementations, and introduced some new technical methods. Finally we made a number of issues required further study.