Content Based Video Retrieval Systems with Local Features: A Survey

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.

With the development of multimedia data types and available bandwidth there is huge demand of video retrieval systems as users shift from text based retrieval systems to content based retrieval systems (CBVRS). Selection of extracted features play an important role in content based video retrieval, regardless of video attributes under consideration. Good features selection also allows the time, space and cost saving in the retrieval process. This leads to the interest of selecting only those features that can be extracted from video data and used for indexing and retrieval along with similarity measurement methods. There are certain issues like video store, search, index and retrieve as a trend in area of content based video retrieval systems. In this paper, we have proposed a method. In this paper fractional discrete cosine transform coefficients are used to extract key frames which eventually save space and time with better completeness in the retrieval process.

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.

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.

Recent advances in digital video compression and networks have made videos more accessible than ever. Several content-based video retrieval systems have been proposed in the past. In this chapter, we first review these existing content-based video retrieval systems and then propose a new framework, called ClassView, to make some advances towards more efficient content-based video retrieval. This framework includes: (a) an efficient video content analysis and representation scheme to support high-level visual concept characterization; (b) a hierarchical video classification technique to bridge the semantic gap between low-level visual features and high-level semantic visual concepts; and (c) a hierarchical video database indexing structure to enable video access over large-scale database. Integrating video access with efficient database indexing tree structures has provided a great opportunity for supporting more powerful video search engines.

Content-based video retrieval is a proper solution to handle the video data. But because of their huge volumes and high dimensionality, finding a proper way to organize them for efficient search and retrieval becomes a challenging and important problem. In this paper, we present a framework for content-based video retrieval using the Shot Cluster Tree, while the latter organizes the content of shots in the tree structure. The framework can supply users with two types of queries: query-by-content and query-by-example. We also put up a novel and efficient clustering method for generating the Shot Cluster Tree structure, where shots which are visual similar and time adjacent are grouped into shot groups using the method of Sliding Shot Window firstly and then shot groups are clustered into shot clusters with the simple agglomerative hierarchical clustering method. In addition to constructing the structure of content, the Shot Cluster Tree also provides better ways to video summary and video annotation, which facilitate the video retrieval. An experimental system has been built up. Experiments verify the effectiveness of the proposed approach.

Content-based video retrieval is very interesting point where it can be used in our daily life. Video retrieval is regarded as one of the most important in multimedia research. The development of multimedia data types there is demand of video retrieval system. Video retrieval can be used for video search and browsing which are useful in web applications. Selection of extracted features play an important role in content based video retrieval. There are two types of feature extraction, low level feature extraction and high level feature extraction. Low level feature extraction based on color, shape, texture, spatial relationship. The main goal of this paper is that, user can give the two different types of input in the form of image query and the text query. First one is that give the input in the form of image query and retrieved image which is similar to the query image by using the CBIR algorithm. CBIR is still developing science. Retrieval of images based on visual features such as color, texture and shape. In this paper gives a detail description of a system developed for retrieving images similar to a query image from a different large set of image. Second one is that give the input in the form of text query and retrieved image by using the ABIR technique. ABIR is powerful algorithm for the information retrieval it can utilize their powerful natural language. Annotation is never complete.

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.

The growing capacity of computers, the abundance of digital cameras and the increased connectivity of the world all point to large digital multimedia archives. They include images and videos from the World Wide Web, museum objects, flowers, trademarks, and views from everyday life. The faster they grow, the more prominently needed is the efficient access to the content of the images and videos. In this paper we have given important step of feature extraction, will be discussed in detail such as color, shape and texture information, particularly paying attention to discriminatory power and invariance. Then, we focus on the concepts of indexing and genre classification as intermediate step to sort the data. We pay attention to (interactive) ways to perform browsing and retrieval by means of information visualization and relevance feedback. Methods are being discussed to localize the retrieved objects in images. We adopt a hybrid approach for such text extraction by exploiting a number of characteristics of text blocks in color images and video frames. Our system detects both caption text as well as scene text of different font, size, color and intensity. Such texts are used for retrieval of video clips based on any given keyword. Content-Based Image And Video Retrieval addresses the basic concepts and techniques for designing content-based image and video retrieval 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.

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.

The elementary technology for video retrieval technique in computing is very sensitive to the user's query not to the contents of video. Every user has perspective to the video in the world of videos. So to identify this is difficult using text based video retrieval as this retrieval is based on text as processing input for video retrieval. The better approach for this video retrieval is content based video retrieval which is gaining acceptance in world of videos as a technique. In this paper, a novel approach for increasing the performance of retrieval by using intermediate levels with Block Truncation Coding (BTC) is presented. For this, the experimental data set is built up of 10 different categories in which 50 videos are present per category. In this paper, the approach of generation of feature by using 20 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> frequency frames of videos from data set considered. Each of the extracted frame is intermediated by intermediate blocks (which is considered as 4 and 9) and then BTC applied on each block to get feature vector suing on 7 different color spaces as KLUV YCbCr, YCgCb, YIQ, YUV RGB, and XYZ. The feature vectors generated are processed and best match results are achieved. The similarity criteria used is absolute difference (AD) for calculating similarity or difference among videos and query. The performance of this technique is confirmed with help of cross over points of precision and recall values for each color space. The higher performance is achieved is in YIQ color space using intermediate block level 16.