Extraction of key frames from video using discrete cosine transform

The video retrieval system refers to the task of retrieving the most relevant video collection, given a user query. By applying some feature extraction models the contents of the video can be extracted. With the exponential increase in video data in online and offline databases as well as a huge implementation of multiple applications in health, military, social media, and art, the Content-Based Video Retrieval (CBVR) system has emerged. The CBVR system takes the inner contents of the video frame and analyses features of each frame, through which similar videos are retrieved from the database. However, searching and retrieving the same clips from huge video collection is a hard job because of the presence of complex properties of visual data. Video clips have many frames and every frame has multiple properties that have many visual properties like color, shape, and texture. In this research, an efficient content-based video retrieval system using the AlexNet model of Convolutional Neural Network (CNN) on the keyframes system has been proposed. Firstly, select the keyframes from the video. Secondly, the color histogram is then calculated. Then the features of the color histogram are compared and analyzed for CBVR. The proposed system is based on the AlexNet model of CNN and color histogram, and extracted features from the frames are together to store in the feature vector. From MATLAB simulation results, the proposed method has been evaluated on benchmark dataset UCF101 which has 13320 videos from 101 action categories. The experiments of our system give a better performance as compared to the other state-of-the-art techniques. In contrast to the existing work, the proposed video retrieval system has shown a dramatic and outstanding performance by using accuracy and loss as performance evaluation parameters.

With the development of multimedia data types and available bandwidth there\nis huge demand of video retrieval systems, as users shift from text based\nretrieval systems to content based retrieval systems. Selection of extracted\nfeatures play an important role in content based video retrieval regardless of\nvideo attributes being under consideration. These features are intended for\nselecting, indexing and ranking according to their potential interest to the\nuser. Good features selection also allows the time and space costs of the\nretrieval process to be reduced. This survey reviews the interesting features\nthat can be extracted from video data for indexing and retrieval along with\nsimilarity measurement methods. We also identify present research issues in\narea of content based video retrieval systems.\n

In this paper, a content-based video retrieval (CBVR) system called Bounded Coordinate of Motion Histogram version 2 (BCMH v2) was processed on a distributed computing platform by using Apache Hadoop framework and a real-time distributed storage system using HBase. In fact, the amount of multimedia data is growing exponentially. Most of this data is available in image and video models. Analyzing huge data involves complex algorithms, which leads to challenges in optimizing processing time and data storage capacity. Many content-based video retrieval systems are suitable for processing large video dataset, but they are limited by the computational time and/or storage on a single machine. Thus, this paper presents the effectiveness of the proposed method with the distributed computing platform and its evaluation on the HOLLYWOOD2 video dataset. The experimental results demonstrate the good performances of the proposed approach.

There has been tremendous growth of laparoscopic surgical video archives in the past few years. Finding the surgical videos manually from the database is very troublesome and time consuming. As a consequence, efficient methods for retrieval of such videos from a database are needed. In this paper, a content-based video retrieval (CBVR) system is presented using average intensity, colour and shape feature. In the proposed retrieval system, average intensity, colour and shape feature are used. The ordinal measure introduced by Bhat and Nayar, which is based on ranks rather than of their values is used in this work. This is important in the laparoscopic videos because adjacent frames can be distinguished on the basis of ranking of their average intensities. Experimental results reveal that mean average precision of the proposed technique is better than the existing motion intensity and direction descriptor (MIDD) approach. This reflects the effectiveness of proposed method.

Content Based Video Retrieval (CBVR) is so popular these days, because of the increased utilization of video based analytical systems. Video based analytics is quite effective than image analysis, as a series of actions are captured by the video. This ends up with better decision making ability. The CBVR systems play an important role in boosting the human–computer interaction. This paper presents a multimodal CBVR that takes both the visual and audio information into account for retrieving relevant videos to the user. Two modules are employed by this work to deal with video and audio data. The video data is processed to detect the significant frame from shots and is achieved by Lion Optimization Algorithm (LOA). The features are extracted from the visual data and with respect to the audio data, MHEC and LPCC features are extracted. The extracted features are clustered by Kernelized Fuzzy C Mean (KFCM) algorithm. Finally, the feature database is formed and is utilized in the query matching process during the testing phase. The performance of the proposed work is tested in terms of precision, recall, F-measure and time consumption rates. The proposed CBVR system proves better performance than the existing approaches and is evident through attained results.

With the improvement of mixed media information composes and accessible transfer speed there is immense interest of video retrieving frameworks, as clients move from content based recovery frameworks to content based retrieval frameworks. Determination of removed features assume an imperative job in substance based video retrieving paying little mind to video qualities being under thought. This work assists the up and coming analysts in the field of video retrieving with getting the thought regarding distinctive procedures and strategies accessible for the video recovery. These highlights are proposed for choosing, ordering and positioning as indicated by their potential enthusiasm to the client. Great feature determination likewise permits the time and space expenses of the recovery procedure to be lessened. This overview surveys the fascinating highlights that can be separated from video information for ordering and retrieving alongside likeness estimation techniques. We likewise recognize present research issues in territory of content based video retrieving frameworks.

As the fast development of network and multimedia technology, there are mass of videos on the Internet. The problem of how to browse the large volume of video data is urgent to be solved. Key frame extraction is a very important technology in online video retrieval and video abstraction. Efficient key frame extraction can boost the development of video browsing technology. In this paper, we first review some commonly-used key frame extraction technology, and then propose a improved key frame extraction algorithm, which measures the similarity of two adjacent frames' contents in terms of the information of frame difference and extracts key frames after optimizing the frame difference. The experiment results show that the proposed algorithm can extract key information from online video efficiently, and has a good description in the aspect of motion of the camera lens.

The paper studies the influence on the similarity by extracting and using m from n frames on videos, the purpose is to evaluate the amount of the proportion similarity between them, and propose a new Content-Based Video Retrieval (CBVR) system. The proposed system uses a Bounded Coordinate of Motion Histogram (BCMH) [1] to characterize videos which are represented by spatio-temporal features (eg. motion vectors) and the Fast and Adaptive Bidimensional Empirical Mode Decomposition (FABEMD). However, a global representation of a video is compared pairwise with all those of the videos in the Hollywood2 dataset using the k-nearest neighbors (KNN). Moreover, this approach is adaptive: a training procedure is presented, and an accuracy of 58.1% is accomplished in comparison with the state-of-the-art approaches on the dataset of 1707 movie clips.

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.

Content-based image retrieval (CBIR) systems use multiple image features to represent an image. These systems suffer from curse of dimensionality since a high-dimensional feature vectors are formed to represent images in the target dataset. Reduction in length of feature vector can speed-up the retrieval process, but it reduces the retrieval accuracy. To overcome the aforementioned problem, an efficient layer-wise feature incremental approach for the CBIR system has been proposed. The proposed approach uses three primitive image features namely color, texture, and shape. The retrieval process is accomplished in three layers, at first a layer-complete dataset is searched but only single-feature space is used. Top 10% of images most similar to query image are retained in the second layer. The second layer uses two features for similarity computation, and only 50% of the most similar images are passed to the third layer. Finally, the third layer uses all three features to compute the similarity. Our aim is to reduce the search space at subsequent layers and use multiple features for a reduced dataset at the final layer. The proposed approach is evaluated on four publicly available image datasets. The retrieval results on the basis of precision, recall, and f-score show the performance improvement in comparison to state-of-the-art CBIR systems.

Rapid development of the multimedia and the associated technologies urge the processing of a huge database of video clips. The processing efficiency lies on the search methodologies utilized in the video processing system. Usage of inappropriate search methodologies may make the processing system ineffective. Hence, an effective video retrieval system is an essential pre-requisite for searching relevant videos from a huge collection of videos. In this paper, an effective content based video retrieval system based on some dominant features such as motion, color and edge is proposed. The system is evaluated using the video clips of format MPEG-2 and then precision-recall is determined for the test clip.

최근 멀티미디어 정보의 양이 매우 빠른 속도로 증가함에 따라 비디오 데이터에 대한 다양한 검색은 더욱 중요한 의미를 가지게 되었다. 비디오 데이터를 효율적으로 처리하기 위해서는 비디오 데이터가 가지고 있는 내용에 대한 정보를 데이터베이스에 저장하고 사용자들의 다양한 질의를 처리할 수 있는 의미기반 검색 기법이 요구된다. 기존의 내용기반 비디오 검색 시스템들은 주석기반 검색 또는 특징기반 검색과 같은 단일 방식으로만 검색을 하므로 검색 효율이 낮을 뿐 아니라 완전한 자동 처리가 되지 않아 시스템 관리자나 주석자의 많은 노력을 요구한다. 본 논문에서는 주석기반 검색과 특징기반 검색을 이용하여 대용량의 비디오 데이터에 대한 사용자의 다양한 의미검색을 지원하는 에이전트 기반에서의 자동화되고 통합된 비디오 의미기반 검색 시스템을 제안한다. 사용자의 기본적인 질의와 질의에 의해 추출된 키 프레임의 이미지를 선택함으로써 에이전트는 추출된 키 프레임의 주석에 대한 의미를 더욱 구체화시킨다. 또한, 사용자에 의해 선택된 키 프레임은 질의 이미지가 되어 제안하는 특징기반 검색기법을 통해 가장 유사한 키 프레임을 검색한다. 따라서 의미기반 검색을 통해 비디오 데이터의 검색의 효율을 높일 수 있도록 시스템을 설계한다. According to the rapid increase of multimedia data quantity recently, various means of video data search has been desired. In order to process video data effectively, it is required that the content information of video data is loaded in database and semantic-based retrieval method can be available for various query of users. Currently existent contents-based video retrieval systems search by single method such as annotation-based or feature-based retrieval, and show low search efficiency and requires many efforts of system administrator or annotator form less perfect automatic processing. In this paper, we propose semantic-based video retrieval system which support semantic retrieval of various users by feature-based retrieval and annotation-based retrieval of massive video data. By user's fundamental query and selection of image for key frame that extracted from query, the agent gives the detail shape for annotation of extracted key frame. Also, key frame selected by user become query image and searches the most similar key frame through feature based retrieval method that propose. Therefore, we design the system that can heighten retrieval efficiency of video data through semantic-based retrieval.

Key Frame Extraction Techniques: A Survey

Aiming at the problem of video key frame extraction, a density peak clustering algorithm is proposed, which uses the HSV histogram to transform high-dimensional abstract video image data into quantifiable low-dimensional data, and reduces the computational complexity while capturing image features. On this basis, the density peak clustering algorithm is used to cluster these low-dimensional data and find the cluster centers. Combining the clustering results, the final key frames are obtained. A large number of key frame extraction experiments for different types of videos show that the algorithm can extract different number of key frames by combining video content, overcome the shortcoming of traditional key frame extraction algorithm which can only extract a fixed number of key frames, and the extracted key frames can represent the main content of video accurately.

Preface. Acknowledgments. 1. Introduction. 2. Fundamentals of content-based image and video retrieval. 3. Designing a content-based image retrieval system. 4. Designing a content-based video retrieval system. 5. A survey of content-based image retrieval systems R.C. Veltkamp, M. Tanase. 6. Case study: MUSE. References. Index.