A Robust Estimator of Image Thumbnail and Video Histogram Representation

For browsing and retrieval system, images are represented by thumbnails and video shots are represented by content representations. In order to achieve better visual quality and retrieval performance, the representation estimator is expected to be accurate and robust. From the statistical perspective, representation extraction can be treated as central value estimation. In this paper, we propose an adaptive alpha-trimmed average estimator based on Gaussian distribution hypothesis test (AATA-GDHT). For a set of values, this estimator extracts the representation by trimming extreme values and then averaging the rest. The criterion to distinguish between extreme values and useful data is derived from Gaussian distribution hypothesis test on the basis of global statics. Experimental results from standard images and videos show that our proposed scheme outperforms traditional methods.For the browsing and retrieval system, images are represented by thumbnails and video shots are represented by histogram representations. In order to achieve better visual quality and retrieval performance, the representation estimator is expected to be accurate and robust. From the statistical perspective, representation extraction can be treated as central value estimation. In this paper, we propose an adaptive alpha-trimmed average estimator based on the Gaussian distribution hypothesis test. For a set of values, this estimator extracts the representation by trimming extreme values and then averaging the rest. The criterion adopted to distinguish between extreme values and useful data is derived from the Gaussian distribution hypothesis test on the basis of global statics. Experimental results from standard images and videos show that our proposed scheme outperforms traditional methods

A Study On Information Retrieval Systems

A video is a key component of today's multimedia applications,  including Video Cassette Recording (VCR), Video-on-Demand (VoD), and virtual walkthrough. This happens supplementary with the fast amplification in video skill (Rynson W.H. Lau et al. 2000). Owing to innovation's progress in the  media, computerized TV, and data frameworks, an immense measure of video information is now exhaustively realistic (Walid G. Aref et al. 2003). The startling advancement in computerized video content has made entrée and moves the data in a tremendous video database a muddled and sensible issue (Chih-Wen Su et al. 2005). Therefore, the necessity for creating devices and frameworks that can effectively investigate the most needed video content, has evoked a great deal of interest among analysts. Sports video has been chosen as the prime application in this proposition since it has attracted viewers around the world

Identification, indexing, and retrieval of cardio-pulmonary resuscitation (CPR) video scenes of simulated medical crisis.

Medical simulations, where uncommon clinical situations can be replicated, have proved to provide a more comprehensive training. Simulations involve the use of patient simulators, which are lifelike mannequins. After each session, the physician must manually review and annotate the recordings and then debrief the trainees. This process can be tedious and retrieval of specific video segments should be automated. In this dissertation, we propose a machine learning based approach to detect and classify scenes that involve rhythmic activities such as Cardio-Pulmonary Resuscitation (CPR) from training video sessions simulating medical crises. This applications requires different preprocessing techniques from other video applications. In particular, most processing steps require the integration of multiple features such as motion, color and spatial and temporal constrains. The first step of our approach consists of segmenting the video into shots. This is achieved by extracting color and motion information from each frame and identifying locations where consecutive frames have different features. We propose two different methods to identify shot boundaries. The first one is based on simple thresholding while the second one uses unsupervised learning techniques. The second step of our approach consists of selecting one key frame from each shot and segmenting it into homogeneous regions. Then few regions of interest are identified for further processing. These regions are selected based on the type of motion of their pixels and their likelihood to be skin-like regions. The regions of interest are tracked and a sequence of observations that encode their motion throughout the shot is extracted. The next step of our approach uses an HMM classiffier to discriminate between regions that involve CPR actions and other regions. We experiment with both continuous and discrete HMM. Finally, to improve the accuracy of our system, we also detect faces in each key frame, track them throughout the shot, and fuse their HMM confidence with the region\u27s confidence. To allow the user to view and analyze the video training session much more efficiently, we have also developed a graphical user interface (GUI) for CPR video scene retrieval and analysis with several desirable features. To validate our proposed approach to detect CPR scenes, we use one video simulation session recorded by the SPARC group to train the HMM classifiers and learn the system\u27s parameters. Then, we analyze the proposed system on other video recordings. We show that our approach can identify most CPR scenes with few false alarms

A Literature Study On Video Retrieval Approaches

A detailed survey has been carried out to identify the various research articles available in the literature in all the categories of video retrieval and to do the analysis of the major contributions and their advantages, following are the literature used for the assessment of the state-of-art work on video retrieval. Here, a large number of papershave been studied

Recent Trends in Computational Intelligence

Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications

Automatic Key-frame Extraction From Broadcast Soccer Videos

This paper presents a new approach for broadcast soccer video navigation and summarization based on specific representative images of the video. It also takes into account some soccer video features to better describe these videos. This work considers a special color reduction based on an HSV subquantization and a shot classification approach for soccer videos by exploring the dominant color related to the playground area.2216223Arman, F., Depommier, R., Hsu, A., Chiu, M.-Y., Content-based browsing of video sequences (1994) MULTIMEDIA '94: Proceedings of the Second ACM International Conference on Multimedia, pp. 97-103. , New York, NY USA. 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