The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This article shows the comparison experiment of automatic and manual classification. In the experiment, 80 individuals are invited in the manual group, and four trials for every comparative group are conducted. After analyzing the results, the authors bring forward some suggestion to introduce manual behaviors into video mining to enhance the effect.
This article shows the improvement of automatic cartoon classification. Two new visual features - color component and color kind based on region segmentation - are proposed. Compared to traditional HSV color histogram and texture, experiment using the two new features can achieve better result, with less dimensions and higher mining efficiency.
We propose a Near-Duplicate Keyframe (NDK) retrieval method that can handle extreme zooming and significant object motion. The first stage consists of eliminating false keypoint matches using symmetric property and a ratio of nearest and second-nearest neighbor distances. Then, a pattern coherency score is assigned to each pair of keyframes. These two features are combined through linear discriminant...
Methods for video copy detection are typically based on the use of low-level visual features. However, low-level features may vary significantly for near-duplicates, which are video sequences that have been the subject of spatial or temporal modifications. As such, the use of low-level visual features may be inadequate for detecting near-duplicates. In this paper, we present a new video copy detection...
This paper presents a method which able to integrate audio and visual information for human action scene analysis. The approach is top-down for determining and extracting action scenes in video by analyzing both audio and video data. We proposed a framework for recognizing actions by measuring image and action-based information from video with the following characteristics: feature extraction is done...
A novel classification method of video shot genre based on data-mining has been proposed. Shot boundary detection and key frames extraction are firstly performed. Then, some visual features such as color and motion are extracted for the key frame and shots. Furthermore, decision tree is applied to discover the rules between these features and shots genres from numerous training data. These rules are...
The ability to filter improper content from multimedia sources based on visual content has important applications, since text-based filters are clearly insufficient against erroneous and/or malicious associations between text and actual content. In this paper, we investigate a method for detection of nudity in videos based on a bag-of-visual-features representation for frames and an associated voting...
In this paper we have designed a neural network based movie genres classifier. The Movie classifier characterizes the movie clips into different movie genres. The characterization is based on low level audio-visual features. We have extracted the computable audio-visual features from the movie clips which are inspired by the techniques and film grammars used by many filmmakers to endow specific characteristics...
This work discusses the application of an Artificial Intelligence technique called data extraction and a process-based ontology in constructing experimental qualitative models for video retrieval and detection. We present a framework architecture that uses multimodality features as the knowledge representation scheme to model the behaviors of a number of human actions in the video scenes. The main...
A new approach for interactive video browsing is described. The novelty of the proposed approach is the flexible concept of interactive navigation summaries. Similar to time sliders, commonly used with standard soft video players, navigation summaries allow random access to a video. In addition, they also provide abstract visualizations of the content at a user-defined level of detail and, thus, quickly...
Most previous works on video summarization target on a single video document. With the popularity of video corpus (e.g. news video archives) and Web videos, video article that consists of a set of relevant videos are frequently confronted by users. By the traditional single-document summarization, these videos are treated independently and the results are usually redundant due to the lack of inter-video...
In this paper, we present a new method for classifying shot type in sports video based on visual attention. The problem is important for applications such as video structure analysis and content understanding. In particular, two-stage off-line learning processes perform knowledge extraction of semantic concepts and automatic shot classification, respectively. In the first stage, the extracted prominent...
Interpretation of WCE video is nowadays largely left to the visual inspection of a medical specialist. This tedious and time consuming task could greatly benefit from techniques that automatically classify and exclude from further processing the non-relevant frames in the video. To this aim in this paper the construction of an indicator function that takes high value, whenever there is a sudden change,...
Video scenes provide semantic meanings for video content description and summarization. This paper explores the pair-wise visual cues of near-duplicate objects for link-constraint affinity-propagation without using keyframes. Experiments demonstrate that our method is more capable to identify scenes comparing with non-constrained clustering algorithms.
Shot boundary detection (SBD) is the basis of interpreting video content, event, and relevant knowledge. As existing SBD algorithms are sensitive to video object motion and no reliable solution exists to provide accurate shot boundary detection, it still remains an unsolved problem. We propose a new algorithm of shot boundary detection in this paper, which employs support vector machine (SVM) as a...
In this paper, we present a new design for an interactive information service based on on-line recognition of the handwriting and quick news stories browsing. A person communicates with server PC using PDA and Bluetooth headset technology in order to consult same key frame that represent a summaries of video news. The result of the server research will by returned to the PDA.
In this paper, we propose a framework to model video sequences using spatiotemporal description of video shots. Spatiotemporal volumes are extracted thanks to an efficient segmentation algorithm. Video shots are described by building an adjacency graph which models the visual properties of the volumes and the spatiotemporal relationships between them. The cost of extracting visual descriptors for...
This paper presents a method which able to integrate audio and visual information for action scene analysis in any movie. The approach is top-down for determining and extract action scenes in video by analyzing both audio and video data. In this paper, we directly modelled the hierarchy and shared structures of human behaviours, and we present a framework of the hidden Markov model based application...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.