Digital video is becoming an emerging force in current computer and telecommunication industries for its large mass of data. video segmentation and key-frame extraction have become crucial for the development of advan...
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ISBN:
(纸本)9781479965458
Digital video is becoming an emerging force in current computer and telecommunication industries for its large mass of data. video segmentation and key-frame extraction have become crucial for the development of advanced digital video systems. Key frame extraction is a very useful technique to provide a concise access to the video content and is the first step towards efficient browsing and retrieval in videodatabases. Existing approaches are either computationally expensive or ineffective in capturing salient visual content. The proposed system extracts key frames from input videos using two distinct, cost-effective algorithms namely reference based key frame extraction and clustering. It uses multiple characteristics such as co-relation, optical flow and mutual information to identify and extract key frames. The proposed system is able to extract the key frames efficiently for any video format & the extracted key frames can satisfactorily represent the salient content of the video. storage is reduced by one-eighth of the total space required by the original video and the original content can be represented in one-fourth the time of the input video achieving very high compression efficiency & hence can be used in any videoretrieval applications.
We study the challenges of image-based retrieval when the database consists of videos. This variation of visual search is important for a broad range of applications that require indexing videodatabases based on thei...
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ISBN:
(纸本)9781479957521
We study the challenges of image-based retrieval when the database consists of videos. This variation of visual search is important for a broad range of applications that require indexing videodatabases based on their visual contents. We present new solutions to reduce storage requirements, while at the same time improving video search quality. The video database is preprocessed to find different appearances of the same visual elements, and build robust descriptors. Compression algorithms are developed to reduce system's storage requirements. We introduce a dataset of CNN broadcasts and queries that include photos taken with mobile phones and images of objects. Our experiments include pairwise matching and retrieval scenarios. We demonstrate one order of magnitude storage reduction and search quality improvements of up to 12% in mean average precision, compared to a baseline system that does not make use of our techniques.
Large amounts of databases are created due to the developments in data storage and acquisition technologies. There is a need to develop an appropriate system that will manage these entire databases. Also we need to pr...
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ISBN:
(纸本)9781479953653
Large amounts of databases are created due to the developments in data storage and acquisition technologies. There is a need to develop an appropriate system that will manage these entire databases. Also we need to precisely and effectively retrieve images from these databases for various applications. The Content Based imageretrieval (CBIR) system serves this purpose. In this paper, we introduce a user based system for CBIR in which genetic algorithm is applied. The different features of color image such as mean, standard deviation and the image bitmap are used for retrieval. In addition, the texture features such as the edge histogram of an image and the entropy of the gray level co-occurrence matrix are used. Furthermore, the genetic algorithm is applied to help the user in identifying the images which satisfy his needs for reducing the gap between the users' expectation and the retrieval results.
In web-scale imageretrieval, the most effective strategy is to aggregate local descriptors into a high dimensionality signature and then reduce it to a small dimensionality. Thanks to this strategy, web-scale image d...
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ISBN:
(纸本)9781479957521
In web-scale imageretrieval, the most effective strategy is to aggregate local descriptors into a high dimensionality signature and then reduce it to a small dimensionality. Thanks to this strategy, web-scale imagedatabases can be represented with small index and explored using fast visual similarities. However, the computation of this index has a very high complexity, because of the high dimensionality of signature projectors. In this work, we propose a new efficient method to greatly reduce the signature dimensionality with low computational and storage costs. Our method is based on the linear projection of the signature onto a small subspace using a sparse projection matrix. We report several experimental results on two standard datasets (Inria Holidays and Oxford) and with 100k image distractors. We show that our method reduces both the projectors storage cost and the computational cost of projection step while incurring a very slight loss in mAP (mean Average Precision) performance of these computed signatures.
This paper addresses the problem of retrieving those shots from a database of video sequences that match a query image. Existing architectures match the images using a high-level representation of local features extra...
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ISBN:
(纸本)9781479946037
This paper addresses the problem of retrieving those shots from a database of video sequences that match a query image. Existing architectures match the images using a high-level representation of local features extracted from the video database, and are mainly based on Bag of Words model. Such architectures lack however the capability to scale up to very large databases. Recently, Fisher vectors showed promising results in large scale imageretrieval problems, but it is still not clear how they can be best exploited in video-related applications. In our work, we use compressed Fisher vectors to represent the video shots and we show that inherent correlation between video frames can be effectively exploited. Experiments show that our proposed system achieves better performance while having lower computational requirements than similar architectures.
We consider the problem of extracting descriptors that represent visually salient portions of a video sequence. Most state-of-the-art schemes generate video descriptors by extracting features, e.g., SIFT or SURF or ot...
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ISBN:
(纸本)9781479957521
We consider the problem of extracting descriptors that represent visually salient portions of a video sequence. Most state-of-the-art schemes generate video descriptors by extracting features, e.g., SIFT or SURF or other keypoint-based features, from individual video frames. This approach is wasteful in scenarios that impose constraints on storage, communication overhead and on the allowable computational complexity for video querying. More importantly, the descriptors obtained by this approach generally do not provide semantic clues about the video content. In this paper, we investigate new feature-agnostic approaches for efficient retrieval of similar video content. We evaluate the efficiency and accuracy of retrieval when k-means clustering is applied to image features extracted from video frames. We also propose a new approach in which the extraction of compact video descriptors is cast as a Non-negative Matrix Factorization (NMF) problem. Initial experiments on video-based matching suggest that compact descriptors obtained via low-rank matrix factorization improve discriminability and robustness to parameter selection compared to k-means clustering.
Multimedia database can be define as a collection of storage and retrieval systems, in which large amount of media objects are created, modified, searched and retrieved, where as Multimedia is the combination of text,...
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Multimedia database can be define as a collection of storage and retrieval systems, in which large amount of media objects are created, modified, searched and retrieved, where as Multimedia is the combination of text, image, graphics, animations, audio and video. The extension of database application to handle multimedia objects requires synchronization of multiple media data streams. Multimedia data mining refers to the extraction of implicit knowledge, data relationships, or other patterns which are not stored in multimedia files explicitly. The system's overall performance in retrieval can be increase by indexing and classification of multimedia data with efficient information fusion of the different modalities is mandatory. Apart from text retrieval, the current waves in web searching and multimedia data retrieval are the search for and delivery of 3D scenes, images, music and video. The content-based multimedia information retrieval provides new techniques and methods for searching various multimedia databases over the world.
In this paper a fuzzy based approach is used for Effective retrieval of video. Initially a video is partitioned into frames and then we use fuzzy logic method in order to retrieve a video. While existing search engine...
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In this paper a fuzzy based approach is used for Effective retrieval of video. Initially a video is partitioned into frames and then we use fuzzy logic method in order to retrieve a video. While existing search engines like Google and yahoo provide a video retrieving through manual textual queries. We provide an advanced concept of giving a relevant image as an input in order to rescue the video. While text based queries uses color histograms our concept makes great advantage of fuzzy logic in order to provide an accurate result.
Local features have been widely used in many computer vision related researches, such as near-duplicate image and videoretrieval. However, the storage and query cost of local features become prohibitive on large-scal...
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Local features have been widely used in many computer vision related researches, such as near-duplicate image and videoretrieval. However, the storage and query cost of local features become prohibitive on large-scale database. In this paper, we propose a representative local features mining method to generate a compact but more effective feature subset. First, we do an unsupervised annotation for all similar images(or frames in video) in the database. Second, we compute a comprehensive score for every local feature. The score function combines the robustness and discrimination. Finally, we sort all the local features in an image by their scores and the low-score local features can be removed. The selected local features are robust and discriminative, which can guarantee the better retrieval quality than using full of the original feature set. By our method, the number of local features can be significantly reduced and a large amount of storage and computational cost can be saved. The experimental results show that we can use 30% of the features to get a better query performance than that of full feature set.
In recent years, the explosion of the data such as text, image, audio, video, data centers and backup data lead to a lot of problem in both storage and retrieval process. The enterprises invest lot of money for storin...
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In recent years, the explosion of the data such as text, image, audio, video, data centers and backup data lead to a lot of problem in both storage and retrieval process. The enterprises invest lot of money for storing the data. Hence, an efficient technique is needed for handling the enormous data. There are two existing techniques for eliminating the redundant data in the storage system such as data deduplication and data reduction. Data deduplication is one of the best technique which eliminates redundant data, reduces the bandwidth and also minimizes the disk usage and cost. various research papers have been studied from the literature, as the result, this paper attempts to summarize various storage optimization techniques, concepts and categories using data deduplication. In addition to this, chunk based data deduplication techniques are surveyed in detail.
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