Classification Of Clips Research Articles

Toward a Fair Evaluation and Analysis of Feature Selection for Music Tag Classification

Accurate music tag classification of music clips has been attracting great attention recently, because it allows one to provide various music excerpts, including unpopular ones, to users based on the clips’ acoustic similarities. Given a user’s preferred music, acoustic features are extracted and then fed into the classifier, which outputs the related tag to recommend new music. Furthermore, the accuracy of the tag classifiers can be improved by selecting the best feature subset based on the domain to which the tag belongs. However, recent studies have struggled to evaluate the superiority of various classifiers because they utilize different feature extractors. In this study, to conduct a direct comparison of existing methods of classification, we create 20 music datasets with the same acoustic feature structure. In addition, we propose an effective evolutionary feature selection algorithm to evaluate the effectiveness of feature selection for tag classification. Our experiments demonstrate that the proposed method improves the accuracy of tag classification, and the analysis with multiple datasets provides valuable insights, such as the important features for general music tag classification in target domains.

Enhanced Video Classification System Using a Block-Based Motion Vector

The main objective of this work was to design and implement a support vector machine-based classification system to classify video data into predefined classes. Video data has to be structured and indexed for any video classification methodology. Video structure analysis involves shot boundary detection and keyframe extraction. Shot boundary detection is performed using a two-pass block-based adaptive threshold method. The seek spread strategy is used for keyframe extraction. In most of the video classification methods, selection of features is important. The selected features contribute to the efficiency of the classification system. It is very hard to find out which combination of features is most effective. Feature selection makes relevance to the proposed system. Herein, a support vector machine-based classifier was considered for the classification of video clips. The performance of the proposed system considered six categories of video clips: cartoons, commercials, cricket, football, tennis, and news. When shot level features and keyframe features, along with motion vectors, were used, 86% correct classification was achieved, which was comparable with the existing methods. The research concentrated on feature extraction where combination of selected features was given to a classifier to get the best classification performance.

Integrated Localization and Recognition for Inshore Ships in Large Scene Remote Sensing Images

Automatic inshore ship recognition, which includes target localization and type recognition, is an important and challenging task. However, existing ship recognition methods mainly focus on the classification of ship samples or clips. These methods rely deeply on the detection algorithm to complete localization and recognition in large scene images. In this letter, we present an integrated framework to automatically locate and recognize inshore ships in large scene satellite images. Different from traditional object recognition methods using two steps of detection-classification, the proposed framework could locate inshore ships and identify types without the detection step. Considering ship size is a useful feature, a novel multimodel method is proposed to utilize this feature. And an Euclidean-distance-based fusion strategy is used to combine candidates given by models. This fusion strategy could effectively separate side-by-side ships. To handle large scene images efficiently, scale-invariant feature transform registration is also integrated into the framework to utilize geographic information. All of these make the framework an end-to-end fashion which could automatically recognize inshore ships in large scene satellite images. Experiments on Quickbird images show that this framework could achieve the actual applied requirements.

Feature extraction for classification of different weather conditions

Classification of different weather conditions provides a first step support for outdoor scene modeling, which is a core component in many different applications of outdoor video analysis and computer vision. Features derived from intrinsic properties of the visual effects of different weather conditions contribute to successful classification. In this paper, features representing both the autocorrelation of pixel-wise intensities over time and the max directional length of rain streaks or snowflakes are proposed. Based on the autocorrelation of each pixel’s intensities over time, two temporal features are used for coarse classification of weather conditions according to their visual effects. On the other hand, features are extracted for fine classification of video clips with rain and snow. The classification results on 249 video clips associated with different weather conditions indicate the effectiveness of the extracted features, by using C-SVM as the classifier.

An examination of automatic video retrieval technology on access to the contents of an historical video archive

Purpose – This paper aims to provide an initial understanding of the constraints that historical video collections pose to video retrieval technology and the potential that online access offers to both archive and users. Design/methodology/approach – A small and unique collection of videos on customs and folklore was used as a case study. Multiple methods were employed to investigate the effectiveness of technology and the modality of user access. Automatic keyframe extraction was tested on the visual content while the audio stream was used for automatic classification of speech and music clips. The user access (search vs browse) was assessed in a controlled user evaluation. A focus group and a survey provided insight on the actual use of the analogue archive. The results of these multiple studies were then compared and integrated (triangulation). Findings – The amateur material challenged automatic techniques for video and audio indexing, thus suggesting that the technology must be tested against the material before deciding on a digitisation strategy. Two user interaction modalities, browsing vs searching, were tested in a user evaluation. Results show users preferred searching, but browsing becomes essential when the search engine fails in matching query and indexed words. Browsing was also valued for serendipitous discovery; however the organisation of the archive was judged cryptic and therefore of limited use. This indicates that the categorisation of an online archive should be thought of in terms of users who might not understand the current classification. The focus group and the survey showed clearly the advantage of online access even when the quality of the video surrogate is poor. The evidence gathered suggests that the creation of a digital version of a video archive requires a rethinking of the collection in terms of the new medium: a new archive should be specially designed to exploit the potential that the digital medium offers. Similarly, users' needs have to be considered before designing the digital library interface, as needs are likely to be different from those imagined. Originality/value – This paper is the first attempt to understand the advantages offered and limitations held by video retrieval technology for small video archives like those often found in special collections.