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Content Based Video Retrieval

Content Based Video Retrieval

Searching for digital information on web, especially images, music, and video, is quickly gaining importance for business and entertainment industry. Content-based video retrieval (CBVR) is now becoming a prominent research interest [8].CBVR is the application of computer vision techniques to video retrieval problem, i.e. searching for digital videos in large databases. "Content-based" means that the search analyzes the contents of the video rather than the metadata such as keywords, tags, or descriptions associated with the video. The term "content" in this context might refer to colors, shapes, textures, or any other information that can be derived from the video itself. Having humans manually annotate videos by entering keywords or metadata in a large database can be time consuming and may not capture the keywords desired to describe the video .In addition to that, manually entering the textual information about the video to be retrieved can be impractical for very large databases. Many standards have been developed to categorize videos, but still face scaling and wrong categorization issues. The evaluation of the effectiveness of video retrieved is subjective and has not been well-defined. In the same regard, CBVR systems have similar challenges in defining success .Most study on CBVR was based on extracting visual features such as color, texture, shape and motion, etc. However, despite all the research efforts, the retrieval accuracy of today’s CBVR algorithms is still very limited.
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A Survey on: Content Based Video Classification

A Survey on: Content Based Video Classification

ABSTRACT:As we know that the today’s world is the Internet world, the amount of the generation of multimedia data is very vast. The sheer size of video data makes it impossible for the human to classify it manually into different classes so the user gets appropriate result. With such large growth of video data, Content-based video classification comes into the picture. Content based video classification and retrieval have attracted more and more focused in last decade. Although successful results were acquired in recent year, automaticanalysing the semantic content of the video is still very challenging at the current state-of-the-art. In order to map the low-level feature to high-level semantic content, many efforts are lead to the semantic indexing and modeling of video content through semi-automatic approach. In this paper, some recent advances in content-based video classification and retrieval are reviewed.
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PERFORMANCE ANALYSIS OF CONTENT BASED VIDEO RETRIEVAL SYSTEM USING CLUSTERING

PERFORMANCE ANALYSIS OF CONTENT BASED VIDEO RETRIEVAL SYSTEM USING CLUSTERING

The multimedia storage grows and the cost for storing multimedia data is cheaper. So there is huge number of videos available in the video repositories. It is difficult to retrieve the relevant videos from large video repository as per user in-terest as users shift from text based retrieval systems to content based retrieval systems. Video retrieval is very important in multimedia database management. This paper offers an overview of the landscape of general strategies in visual content-based video retrieval, focusing on methods for video structure analysis, including shot boundary detection, key frame extraction, extraction of features including static key frame features, object feature, video retrieval including similarity measures and the proposed procedure consists of the unique aspect of clustering techniques.
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Content based video indexing and retrieval using motion features

Content based video indexing and retrieval using motion features

One of the main concepts when dealing with a content based video indexing and retrieval system (as opposed to a still image system) is how a video sequence can be broken into manageable segments. Obviously, when attempting to index and retrieve video, it would be impractical, and unsuitable to index full video sequences (for example a two hour long movie) at once. The video sequence needs to be broken down into shorter lengths. This aim of video indexing systems is to perform this segmentation automatically, and based on the video content rather than on simple time measures.
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Content Based Video Retrieval Using Integrated Feature Extraction and Personalization of Results

Content Based Video Retrieval Using Integrated Feature Extraction and Personalization of Results

Proficient detection and segmentation of text characters from the background is necessary to fill the gapbetween image documents and the input of standard OCR systems [2] .The basic visual features of index frame include colour and texture. Research in content based video retrieval today is a lively disciplined, expanding in breadth Representative features extracted from index frames are stored in feature database and used for object-based video retrieval. Texture is another important property of index frames. Various texture representations have been investigated in pattern recognition and computer vision. [11]
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CONTENT BASED VIDEO RETRIEVAL BASED ON HDWT AND SPARSE REPRESENTATION

CONTENT BASED VIDEO RETRIEVAL BASED ON HDWT AND SPARSE REPRESENTATION

Video retrieval has recently attracted a lot of research attention due to the exponential growth of video datasets and the internet. Content based video retrieval (CBVR) systems are very useful for a wide range of applications with several type of data such as visual, audio and metadata. In this paper, we are only using the visual information from the video. Shot boundary detection, key frame extraction, and video retrieval are three important parts of CBVR systems. In this paper, we have modified and proposed new methods for the three important parts of our CBVR system. Meanwhile, the local and global color, texture, and motion features of the video are extracted as features of key frames. To evaluate the applicability of the proposed technique against various methods, the P(1) metric and the CC_WEB_VIDEO dataset are used. The experimental results show that the proposed method provides better performance and less processing time compared to the other methods.
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Entropy Supported Video Indexing for Content based Video Retrieval

Entropy Supported Video Indexing for Content based Video Retrieval

Explosive growth of digital content including image, audio and video on web and as well as on desktop applications has demanded development of new technologies and methods for representation, storage and retrieval of multimedia data. Rapid development of digital libraries and repositories are attempting to achieve efficient techniques for the same. Despite of many initial successes problems persist in the area of effective video retrieval system since decades. Many of the video retrieval systems are presently based on the metadata attributes like name, date of creation, tagged words, annotation, etc. This however leads to unsatisfactory results to users. Content Based Video Retrieval (CBVR) system works more effectively as these deals with content of video rather than video metadata. The increased availability and usage of on-line digital video has created a need for automated video content analysis techniques including indexing and retrieving.
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Content based Video Retrieval: A Survey

Content based Video Retrieval: A Survey

Videos are a powerful and communicative media that can capture and present information. The rapidly expanding digital video information has motivated growth of new technologies for effective browsing, annotating and retrieval of video data. Content-based video retrieval has attracted wide research during the last 10 years. Users are more diverted to content based search rather than text based search. These lead to the process of selecting, indexing and ranking the database according to the human visual perception. This paper reviews the recent research in content based video retrieval system. This survey focusing on video structure analysis, like, shot boundary detection and key frame extraction, different feature extraction methods including SIFT, SURF, etc, similarity measure, video indexing, and video browsing. This system retrieves similar videos based on local feature descriptor called SURF (Speeded-Up Robust Feature). For image convolution SURF relies on integral images. In SURF we use Hessian matrix-based measure for the detector and a distribution-based descriptor. SURF can be computed and compared much faster with respect to repeatability, uniqueness and robustness. SURF is better than previous proposed methods as SIFT, PCA-SIFT, GLOH, etc. Finally the future scope in this system is specified.
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A REVIEW ON CONTENT BASED VIDEO RETRIEVAL, CLASSIFICATION AND SUMMARIZATION

A REVIEW ON CONTENT BASED VIDEO RETRIEVAL, CLASSIFICATION AND SUMMARIZATION

By definition, a Content-Based Video Retrieval (CBVR) system aims at assisting a human operator to retrieve sequence (target) within a potentially large database [11]. The authors in [11] has just presented a natural extension of the well-known Content-Based Image Indexing and Retrieval (CBIR) systems. Both systems are aiming at accessing image and video by its content, namely, the spatial (image) and spatial-temporal (video) information. A Typical spatial information includes texture, color, edge, etc., while a typical temporal information includes change of scenes and motions. Moving from images to video adds several orders of complexity to the retrieval problem due to indexing, analysis and browsing over the inherently temporal aspect of video [15].
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Content Based Video Activity Classifier

Content Based Video Activity Classifier

computer vision is the ability to understand human activity, look and behaviour from a video. The human activity recognition has gained a variety of applications such as content base retrieval, human motion analysis, electronic video surveillance and visual enhancement. Selection of extracted features plays an important role in content-based video activity classifier. These features are intended for selecting, indexing and ranking according to the potential interest to the user. The major problem here is that how to analyse and search within videos based on their content, with minimum human intervention. The proposed method aims to develop such a system where user will be able to search based on the semantic content of the video like in a query response mechanism.
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An Enhanced Strategy for Efficient Content
          Based Video Copy Detection

An Enhanced Strategy for Efficient Content Based Video Copy Detection

For attaining both efficiency and effectiveness in video copy detection, the feature signature should adhere to two crucial properties, uniqueness and robustness. Uniqueness stipulates discriminating potential of the feature. Robustness implies potential of noise resistance means features should remain unchanged even in case of different photometric or geometric transformations. Once set of keyframes has been decided, distinct features are extracted from keyframes and used to create signature of a video. We mainly focus on visual features suitable for video copy detection, includes spatial features of keyframes, temporal features and motion features of video sequence. Spatial features of keyframes are categorized into global and local features.
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Content based Video Querying Technique for Video Retrieval and Video Making from Large Video Compilation

Content based Video Querying Technique for Video Retrieval and Video Making from Large Video Compilation

Based on the feature extraction algorithm and semantic feature identification this applies a motion matching alignment scheme image alignment and video making with extracted clips in the[r]

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Survey on Content-Based Lecture Video Retrieval By Text

Survey on Content-Based Lecture Video Retrieval By Text

Content-Based Video Retrieval (CBVR) has been increasingly used to describe the process of retrieving desired videos from a large collection on the basis of features that are extracted from the videos. The extracted features are used to index, classify and retrieve desired and relevant videos while filtering out undesired ones. Videos can be represented by their audio, texts, faces and objects in their frames. An individual video possesses unique motion features, color histograms, motion histograms, text features, audio features, features extracted from faces and objects existing in its frames. Videos containing useful information and occupying significant space in the databases are under-utilized unless CBVR systems capable of retrieving desired videos by sharply selecting relevant while filtering out undesired videos exist. Results have shown performance improvement (higher precision and recall values) when features suitable to particular types of videos are utilized wisely. Various combinations of these features can also be used to achieve desired performance. In this paper, a complex and wide area of CBVR and CBVR systems have been presented in a comprehensive and simple way. Processes at different stages in CBVR systems are described in a systematic way. Types of features, their combinations and their utilization methods, techniques and algorithms are also shown. Various querying methods, some of the features like GLCM, Gabor Magnitude, an algorithm to obtain similarities like Kullback-Leibler distance method and Relevance Feedback Method are discussed.
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Frame based Video Retrieval using Video Signatures

Frame based Video Retrieval using Video Signatures

The World Wide Web today has grown so wide and the video-on-demand applications and video share web are becoming very popular day-by-day on the World Wide Web. An efficient video similarity search algorithm for content- based video retrieval is important in video-on-demand based services. However, there is no satisfying video similarity search algorithm showing cent percentage performance. It is proposed here to implement a video similarity measure algorithm based on the color-features of each video represented by a compact fixed size representation known as Video Signature. This Video signature which is based on the image signature is computed on the basis of YCbCr Histogram and the sum of its weighted means. The video signatures of videos are then used to find the similar videos in-terms of visually similar frames, by using the range. This method of similarity measure is assumed to be efficient in various aspects.
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Title: A SURVEY ON CONTENT BASED LECTURING VIDEO RETRIEVAL

Title: A SURVEY ON CONTENT BASED LECTURING VIDEO RETRIEVAL

metadata from visual as well as audio resources of lecture videos automatically is done by applying appropriate analysis techniques. For evaluation purposes, several automatic indexing functionalities is developed in a large lecture video portal, which can guide both visually and text oriented users to navigate within lecture video. A user study that intended to verify their search hypothesis and to investigate the usability and the effectiveness of proposed video indexing feature. In this paper, we are presenting Content Based Video Retrieval (CBVR) System it includes various steps: Video Segmentation: Adaptive Thresholding algorithm is used for image segmentation, Feature Extraction: Features are extracted for the key frame and stored into feature vector. Histogram Of Gradient is an algorithm which is used for the feature extraction. Classification: It is a learning algorithm which is used for detection and finally performance analysis is done based on the processing speed of feature extraction of videos and frames, and their effectiveness evaluation of videos.
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An Efficient Method to Retrieve Content Based Lecture Video Using Speech and Video Text Information

An Efficient Method to Retrieve Content Based Lecture Video Using Speech and Video Text Information

In the proposed system we extract metadata from visual as well as audio resources of lecture videos automatically by applying appropriate analysis techniques. For visual analysis, it propose a new method for slide video segmentation and apply video OCR to gather text Metadata. Lecture outline is extracted from OCR transcripts by using stroke width and geometric information. Subsequently, it will extract textual metadata by applying video Optical Character Recognition (OCR) technology on key-frames and Automatic Speech Recognition (ASR) on lecture audio tracks. The OCR and ASR transcript and detected slide text line types are adopted for keyword extraction, by which both video- and segment-level keywords are extracted for content-based video browsing and search. We propose a solution for automatic German phonetic dictionary generation, which fills the gap in open-source ASR domain. The dictionary software and compiled speech corpus are provided for the further research use.
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Video Liveness Verification and Content Base Video Retrieval

Video Liveness Verification and Content Base Video Retrieval

_________________________________________________________________________________________________ Abstract-: The ubiquitous and connected nature of camera loaded mobile devices has greatly estimated the value and importance of visual information they capture. Today, sending videos from camera phones uploaded by unknown users is relevant on news networks, and banking customers expect to be able to deposit checks using mobile devices. In this paper we represent Movee, a system that addresses the fundamental question of whether the visual stream exchange by a user has been captured live on a mobile device, and has not been tampered with by an adversary. Movee leverages the mobile device motion sensors and the inherent user movements during the shooting of the video. Movee exploits the observation that the movement of the scene recorded on the video stream should be related to the movement of the device simultaneously captured by the accelerometer. the last decade e-lecturing has become more and more popular. The amount of lecture video data on the World Wide Web (WWW) is increasing rapidly. Therefore, a more efficient method for video retrieval in WWW or within large lecture video archives is immediately needed. This paper shows an method for automated video indexing and video search in large lecture video archives. First of all, we apply automatic video segmentation and key-frame revelation to offer a visual guideline for the video content navigation. Subsequently, we abstract textual metadata by applying video Optical Character Realization (OCR) technology on key-frames and Automatic Speech Realization (ASR) on lecture audio tracks. The OCR and ASR transcript as well as encounter slide text line types are adopted for keyword extraction, by which both video- and segment-level keywords are extracted for content-based video browsing and search. The performance and the effectiveness of proposed indexing functionalities is proven by evaluation. The OCR and ASR transcript as well as detected slide text line types are fitting for keyword extraction, by which both video- and segment-level keywords are extracted for content-based video browsing and search. The performance and the efficiency of proposed indicating functionalities is proven by evaluation.
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VIDEO FOOTAGE RETRIEVAL SYSTEM USING FEATURE PROFILE ANALYSIS ON TIME AXIS.

VIDEO FOOTAGE RETRIEVAL SYSTEM USING FEATURE PROFILE ANALYSIS ON TIME AXIS.

Content-based video retrieval (CBVR) is the application of computer vision techniques to the video footage image retrieval problem i.e. searching for digital video content in long time recording of a spot using close circuit TV camera e.g. in ATMs, shopping malls etc. Content-based method means that the search analyzes the contents of the video recording rather than the metadata search keywords, tags, or descriptions associated with the recording. The term "content" in this context refers to any unusual activity in an event for long time, like staying of a person for a long time or more than expected time of a person in an ATM.
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ADEPT IDENTIFICATION OF SIMILAR VIDEOS FOR WEB-BASED VIDEO SEARCH

ADEPT IDENTIFICATION OF SIMILAR VIDEOS FOR WEB-BASED VIDEO SEARCH

We propose an efficient CBVR (Content based video retrieval), for identifying and retrieving similar videos from very large video database.. Here searching is based on[r]

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Shearlet Based Video Fingerprint for Content Based Copy Detection

Shearlet Based Video Fingerprint for Content Based Copy Detection

Content-based copy detection (CBCD) is widely used in copyright control for protecting unautho- rized use of digital video and its key issue is to extract robust fingerprint against different at- tacked versions of the same video. In this paper, the “natural parts” (coarse scales) of the Shearlet coefficients are used to generate robust video fingerprints for content-based video copy detection applications. The proposed Shearlet-based video fingerprint (SBVF) is constructed by the Shearlet coefficients in Scale 1 (lowest coarse scale) for revealing the spatial features and Scale 2 (second lowest coarse scale) for revealing the directional features. To achieve spatiotemporal natural, the proposed SBVF is applied to Temporal Informative Representative Image (TIRI) of the video se- quences for final fingerprints generation. A TIRI-SBVF based CBCD system is constructed with use of Invert Index File (IIF) hash searching approach for performance evaluation and comparison using TRECVID 2010 dataset. Common attacks are imposed in the queries such as luminance at- tacks (luminance change, salt and pepper noise, Gaussian noise, text insertion); geometry attacks (letter box and rotation); and temporal attacks (dropping frame, time shifting). The experimental results demonstrate that the proposed TIRI-SBVF fingerprinting algorithm is robust on CBCD ap- plications on most of the attacks. It can achieve an average F1 score of about 0.99, less than 0.01% of false positive rate (FPR) and 97% accuracy of localization.
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