SHOT CLASSIFICATION FOR CONTENT-BASED INDEXING

IEEE Transactions on Multimedia, 2004

Recent advances in digital video compression and networks have made video more accessible than ever. However, the existing content-based video retrieval systems still suffer from the following problems. 1 ) Semantics-sensitive video classification problem because of the semantic gap between low-level visual features and high-level semantic visual concepts; 2) Integrated video access problem because of the lack of efficient video database indexing, automatic video annotation, and concept-oriented summary organization techniques. In this paper, we have proposed a novel framework, called ClassView, to make some advances toward more efficient video database indexing and access. 1) A hierarchical semantics-sensitive video classifier is proposed to shorten the semantic gap. The hierarchical tree structure of the semantics-sensitive video classifier is derived from the domain-dependent concept hierarchy of video contents in a database.

IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2000

Video indexing and retrieval have a wide spectrum of promising applications, motivating the interest of researchers worldwide. This paper offers a tutorial and an overview of the landscape of general strategies in visual content-based video indexing and retrieval, focusing on methods for video structure analysis, including shot boundary detection, key frame extraction and scene segmentation, extraction of features including static key frame features, object features and motion features, video data mining, video annotation, video retrieval including query interfaces, similarity measure and relevance feedback, and video browsing. Finally, we analyze future research directions.

Multimedia Tools and Applications, 1997

The increasing development of advanced multimedia applications requires new technologies for organizing and retrieving by content databases of still digital images or digital video sequences. To this aim image and image sequence contents must be described and adequately coded. In this paper we describe a system allowing content-based annotation and querying in video databases. No user action is required during the database population step. The system automatically splits a video into a sequence of shots, extracts a few representative frames (said r-frames) from each shot and computes r-frame descriptors based on color, texture and motion. Queries based on one or more features are possible. Very interesting results obtained during the severe tests the system was subjected to are reported and discussed.

International Journal of Computer Applications, 2012

Optimal efficiency of the retrieval techniques depends on the search methodologies that are used in the video processing system. The use of inappropriate search methodologies may make the processing system ineffective. Hence, an effective video segmentation and retrieval system is an essential prerequisite for searching a relevant video from a huge collection of videos. In this paper we propose a video retrieval system based on the integration of various visual cues. In contrast to key-frame based representation of shot, our approach analyzes all frames within a shot to construct a compact representation of video shot. In feature extraction step we extract quantized color, motion and edge density features. A similarity measure is defined using LSI (Latent semantic indexing) to locate the occurrence of similar video clips in the database. Our approach is able to fully exploit the spatio-temporal contents of the video. Experimental results indicate that the proposed algorithm is effective and outperforms some existing technique. The detailed result analysis and graphs supports the effectiveness and correctness of the system.

Journal of Theoretical and Applied Information Technology, 2023

Over the past ten years, researchers have studied the Content Based Video Retrieval System, based on numerous applications, advancements, and technologies. In video retrieval systems, there is still a requirement for the high-level semantic elements as and the processing of low level materials. As a result, it inspires and motivates a lot of academics to learn more about the content Retrieval and to make more useful and effective while creating system applications. Analysis of video for retrieval of key aspects is regarded as earlier work. In this instance, input videos from YouTube are watched for analysis. After that, foreground segmentation is carried out to find a lot of tiny subset objects since each subset must identify the foreground video class. Given for feature extraction is the split region. In this study, four distinct methods for extracting features�chromatic moment, blur, color variety, and reflection features are taken into consideration. The high dimensionality characteristics are removed from the retrieved features using Principal Component Analysis since they may affect classification accuracy. The feature vectors are taken into account while combining all of the parts. The Nave Bayes classifier is used to complete the classification process. Metrics including accuracy, precision, recall, and F-measure are used to gauge how effectively video retrieval is performed. The predicted model outperforms the current strategy when the proposed model and the in-depth learning approach are compared.

International Journal of Multimedia Information Retrieval, 2013

This paper presents a genre-specific modeling strategy capable of improving the task of content based video classification and the speed of data retrieval operations. With the ever increasing growth of video data it is important to classify video shots into groups based on its content. For that reason, it is of primary concern to design systems that could automatically classify videos into different genres based on its content. We consider the genre recognition task as a classification problem. We use support vector machines to perform the classification task and propose an improved video classification method. The experimental results show that genre-specific modeling of features can significantly improve the performance. Results have been compared with two contemporary works on video classification, to demonstrate the superiority of our proposed framework.

Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016, 2017

We share the implementation details and testing results for video retrieval system based exclusively on features extracted by convolutional neural networks. We show that deep learned features might serve as universal signature for semantic content of video useful in many search and retrieval tasks. We further show that graph-based storage structure for video index allows to efficiently retrieving the content with complicated spatial and temporal search queries.

J. Inf. Data Manag., 2010

This work is concerned with video indexing and retrieval based on visual features. It puts forth an approach for the automatic summary and indexing of digital videos in order to support queries based on visual content within the indexed video's repository. The proposed approach was applied to a database containing more than 34 hours of broadcast news videos. Visual features extracted from the summarized version of the videos were then used for video content indexing. That provided us with the basis for various experiments and analysis on the retrieval of visual content with the application of various techniques implemented in this work. The approach proposes a method for key frame extraction that summarizes video content in a static storyboard, specifically projectec, for key frame retrieval and video access. Thus, the selected key frames are processed in order to extract statistical features as well as wavelet coefficients to represent the video's essence in a very short am...

International Journal of Innovative Technology and Exploring Engineering, 2019

Videos are recorded and uploaded daily to the sites like YouTube, Facebook etc. from devices such as mobile phones and digital cameras with less or without metadata (semantic tags) associated with it. This makes extremely difficult to retrieve similar videos based on this metadata without using content based semantic search. Content based video retrieval is problem of retrieving most similar videos to a given query video and has wide range of applications such as video browsing, content filtering, video indexing, etc. Traditional video level features based on key frame level hand engineered features which does not exploit rich dynamics present in the video. In this paper we propose a fast content based video retrieval framework using compact spatio-temporal features learned by deep learning. Specifically, deep CNN along with LSTM is deploy to learn spatio-temporal representations of video. For fast retrieval, binary code is generated by hashing learning component in the framework. F...

Similarity Search and Applications 12th International Conference, SISAP 2019, 2019

Since the 1970's the Content-Based Image Indexing and Retrieval (CBIR) has been an active area. Nowadays, the rapid increase of video data has paved the way to the advancement of the technologies in many different communities for the creation of Content-Based Video Indexing and Retrieval (CBVIR). However, greater attention needs to be devoted to the development of effective tools for video search and browse. In this paper, we present Visione, a system for large-scale video retrieval. The system integrates several content-based analysis and retrieval modules , including a keywords search, a spatial object-based search, and a visual similarity search. From the tests carried out by users when they needed to find as many correct examples as possible, the similarity search proved to be the most promising option. Our implementation is based on state-of-the-art deep learning approaches for content analysis and leverages highly efficient indexing techniques to ensure scalability. Specifically, we encode all the visual and textual descriptors extracted from the videos into (surrogate) textual representations that are then efficiently indexed and searched using an off-the-shelf text search engine using similarity functions.