THE ROLE OF METADATA IN WEB VIDEO MINING: ISSUES AND PERSPECTIVES

Now a days, the Data Engineering becoming emerging trend to discover knowledge from web audio-visual data such as- YouTube videos, Yahoo Screen, Face Book videos etc. Different categories of web video are being shared on such social websites and are being used by the billions of users all over the world. The uploaded web videos will have different kind of metadata as attribute information of the video data. The metadata attributes defines the contents and features/characteristics of the web videos conceptually. Hence, accomplishing web video mining by extracting features of web videos in terms of metadata is a challenging task. In this work, effective attempts are made to classify and predict the metadata features of web videos such as length of the web videos, number of comments of the web videos, ratings information and view counts of the web videos using data mining algorithms such as Decision tree J48 and navie Bayesian algorithms as a part of web video mining. The results of Decision tree J48 and navie Bayesian classification models are analyzed and compared as a step in the process of knowledge discovery from web videos.

The astonishing growth of videos on the Internet such as YouTube, Yahoo Screen, Face Book etc, organizing videos into categories is of paramount importance for improving user experience and website utilization. In this information age, video information is the rapidly sharing by the people through social media websites such as YouTube, Face Book, yahoo Screen etc. Different categories of web video are shared on social websites and used by the billions of users all over the world. The classification/partitioning of web videos in terms of length of the video, ratings, age of the video, number of comments etc, and analysis of this web video as a unstructured complex data is a challenging task. In this work we propose effective classification model to classify each category of web-videos (Ex- ‘Entertainment’, ‘People and Blogs’, ‘Sports’, ‘News and Politics’, ‘Science and Technology’ etc) based on other web metadata attributes as splitting criteria. An attempt is made to extract metadata from web videos. Based on the extracted metadata, web videos are classified/partitioned into different categories by applying data mining classification algorithms such as and Random Tree and J48 classification model. The classification results are compared and analyzed using cost/benefit analysis. Also the results demonstrate classification of web videos depends largely on available metadata and accuracy of the classification model. Classification/partitioning of web-based videos are important task with many applications in video search and information retrieval process. However, collecting metadata required for classification model may be prohibitively expensive. The experimental difficulties arise from large data diversity within a category is pitiable of metadata and dreadful conditions of web video metadata.

Nowadays YouTube becoming most popular video sharing website, and is established in 2005. The YouTube official website is providing different categories videos including Science and Technology, Films and Animation, News and politics, Movies, Comedy, Sports, Music etc. Each video hosted in website such as YouTube have its own identity and features. The identity and features of each video can be described by web video metadata objects such as- URL of each video, category, length of the video, rating information, view counts, comment information, key words etc. Using extracted web video metadata objects, we present an in-depth and systematic clustering study on the metadata objects of YouTube videos using Expectation Maximization (EM) and Density Based (DB) clustering approach. Distinct web video metadata object clusters are formed based on different category of web videos. The resultant clusters are analyzed in depth as a step in the KDD process.

The impact of social Medias such as YouTube, Twitter, and FaceBook etc on the modern world is led to huge growth in the size of video data over the cloud and web. The evolution of smart phones/Tabs could be one of the reasons for increasing in the rate of huge video data over the web. Due to the rapid evolution of web videos over the web, it is becoming difficult to identify popular, non-popular and average popular videos without watching the content of it. To cluster web videos based on their metadata into ‗Popular‘, ‗Non-Popular‘, and ‗Average Popular‘ is one of the complex research questions for the Social Media and Computer Science researchers‘. In this work, we propose two effective methods to cluster web videos based on their meta-objects. Large scale web video meta-objects such as- length, view counts, numbers of comments, rating information are considered for knowledge discovery process. The two clustering algorithms-Expectation Maximization (EM) and Distribution Based (DB) clustering are used to form three types of clusters. The resultant clusters are analyzed to find popular video cluster, average popular video cluster and non-popular video clusters. And also the results of EM and DB clusters are compared as a step in the process of knowledge discovery.

Web Multimedia data mining (WMDM) can be defined as the process of finding interesting patterns from media data such as audio, video, image and text that are not ordinarily accessible by basic queries and associated results. MDM is the mining of knowledge and high level multimedia information from large multimedia database system. MDM refers to pattern discovery, rule extraction and knowledge acquisition from multimedia database. To extract knowledge from multimedia database multimedia techniques are used. We compare MDM techniques with the state of the art data mining techniques involving clustering, classification, sequence pattern mining, association rule mining and visualization. This paper is a review on Web multimedia mining (WMM) and Knowledge discovery it elaborates basic concepts, application at various areas, techniques, approaches and other useful areas which need to be work for WMM. Analyzing this huge amount of multimedia data to discover useful knowledge is a challenging problem which has opened the opportunity for research in WMM and knowledge discovery.

In the earliest time with the growth of digital libraries and video databases, it is becoming very important to understand and mine the knowledge and information from video database automatically. Many video mining approaches has been proposed till now for extracting useful knowledge from video database. To find the intended information in a video clip or in a video database is still a difficult and laborious task due to its semantic gap between the low-level characteristics and high-level video meaning concepts. We have done survey on previous paper that are representing the various data mining applications, functionalities, and video features.

Multimedia data mining is a popular research domain which helps to extract interesting knowledge from multimedia data sets such as audio, video, images, graphics, speech, text and combination of several types of data sets. Normally, multimedia data are categorized into unstructured and semi-structured data. These data are stored in multimedia databases and multimedia mining is used to find useful information from large multimedia database system by using various multimedia techniques and powerful tools. This paper provides the basic concepts of multimedia mining and its essential characteristics. Multimedia mining architectures for structured and unstructured data, research issues in multimedia mining, data mining models used for multimedia mining and applications are also discussed in this paper. It helps the researchers to get the knowledge about how to do their research in the field of multimedia mining.

IJCSNS, 2010

Over the past decades, data mining has proved to be a successful approach for extracting hidden knowledge from huge collections of structured digital data stored in databases. From the inception, Data mining was done primarily on numerical set of data. Nowadays as large multimedia data sets such as audio, speech, text, web, image, video and combinations of several types are becoming increasingly available and are almost unstructured or semistructured data by nature, which makes it difficult for human beings to extract the information without powerful tools. This drives the need to develop data mining techniques that can work on all kinds of data such as documents, images, and signals. This paper explores on survey of the current state of multimedia data mining and knowledge discovery, data mining efforts aimed at multimedia data, current approaches and well known techniques for mining multimedia data.

Modern developments in digital media technologies has made transmitting and storing large amounts of multi/rich media data (e.g. text, images, music, video and their combination) more feasible and affordable than ever before. However, the state of the art techniques to process, mining and manage those rich media are still in their infancy. Advances developments in multimedia acquisition and storage technology the rapid progress has led to the fast growing incredible amount of data stored in databases. Useful information to users can be revealed if these multimedia files are analyzed. Multimedia mining deals with the extraction of implicit knowledge, multimedia data relationships, or other patterns not explicitly stored in multimedia files. Also in retrieval, indexing and classification of multimedia data with efficient information fusion of the different modalities is essential for the system's overall performance. The purpose of this paper is to provide a systematic overview of multimedia mining. This article is also represents the issues in the application process component for multimedia mining followed by the multimedia mining models.

2019

Nowadays, multimedia technology is widely applied in everywhere. Data quarrying or mining is an effective and powerful approach for extracting hidden information from enormous collections of synchronized digital data stored in databases. In digital library Data mining is the key technology. It is required to retrieve the information of text and manage and also retrieve the video information. Multimedia Data Mining is the technique to be used to discover the implicit, effective, valuable and intelligible pattern from a large amount of multimedia data by analyzing the feature of seeing and hearing and then to discover knowledge and obtain the tendency and association among the events. And it can also provide us the ability of decision supporting to resolve the problem. This paper discusses the basic theories of Data Mining and its current approaches.