Introduction

Introduction: Multimedia Data refers to data such as text, numeric, images, video, audio, graphical, temporal, relational and categorical data. Multimedia data mining refers to pattern discovery, rule extraction and knowledge acquisition from multimedia database [9]. Multimedia database systems are increasingly common owing to the popular use of audio video equipment, digital cameras, CD-ROMs, and the Internet [12]. These are typically the elements or the building blocks of generalized multimedia environments, platforms, or integrating tools [1]. The main objective of multimedia data mining is the idea of mining data in different kinds of information. Current data mining tools operate on structured data, the kind of data that resides in large relational databases whereas data in the multimedia databases are semi- structured or unstructured. Often compared with data mining, multimedia mining reaches much higher complexity resulting from: (i) The huge volume of data, (ii) The variability and heterogeneity of the multimedia data (e.g. diversity of sensors, time or conditions of acquisition etc.) and (iii) The multimedia content’s meaning is subjective [9]. Multimedia generally gives a lot of data on each entity, but not the same data for each entity [18]. The multimedia is classified in to two categories: (i) Static media such as text, graphics, and images and (ii) Dynamic media such as animation, music, audio, speech, and video. Figure 4 illustrates multimedia data mining, in particular, various aspects of multimedia data mining [10]. Text Mining: Text Mining also referred as text data mining and it is used to find meaningful information from the unstructured texts that are from various sources. Text is the foremost general medium for the proper exchange of information. Text Mining is to evaluate huge amount of usual language text and it detects exact patterns to find useful information [13]. Image Mining: Image mining systems that can be automatically extracted from image data are increasingly in demand. The fundamental challenge in the image mining is to determine how the low level, pixel demonstration contained in a raw image or image sequence can be processed to identify high level spatial objects and correlation. Image mining is the concept used to detect pattern sand extract data from images stored in the large data bases [10]. Audio Mining: Audio mining is a technique by which the content of an audio signal can be automatically analyzed and searched. Itis most commonly used in the field of automatic speech recognition, where the analysis tries to identify any speech within the audio [11]. Video Mining: Video mining is unsubstantiated to find the interesting patterns from large amount of video data; multimedia data is video data such as text, image, and metadata, visual and audio. The processing are indexing, automatic segmentation, content-based retrieval, classification and detecting triggers. It is commonly used in various applications like security and surveillance, entertainment, medicine, sports and education programs. Types Format Techniques Available Text Keywords, Patterns Clustering Image Graphics, Animation Association Rule, Clustering Audio Speech Music Clustering Video Gamming Clustering Table 1. Data types of Multimedia Data Mining Importance of Multimedia data mining: The need to understand large, complex, information-rich data sets is common to virtually all fields of business, science, and engineering. The ability to extract useful knowledge hidden in these data and to act on that knowledge is becoming increasingly important in today's competitive world [18]. Multimedia and data mining are two very interdisciplinary and multidisciplinary areas with independently and simultaneously rapid developments in recent years, for many decision-making applications the need for tools to extract hidden useful knowledge embedded within multimedia collections has become central point for research [15]. An MM-DBMS is essentially a DBMS that manages the multimedia data. Therefore, all of the issues in designing a DBMS apply for an MM-DBMS. That is, we need architectures for MM-DBMSs. [16]. A wide range of systems and algorithms has been proposed over the past few years. Individual proposals are usually submitted to get herewith specific datasets and evaluation methods that prove the superiority of the new algorithm. There is still a need for a common and exhaustive benchmarking system to perform objective testing [17]. Issues in Multimedia data mining Multimedia data mining develops into a conventional, mature and trusted discipline; many still-pending issues have to be addressed. These issues pertain to the multimedia data mining approaches applied and their limitations. Multimedia data consists of a variety of media formats or file representations including TIFF, BMP, PPT, IVUE, FPX, JPEG, MPEG, AVI, MID, WAV, DOC, GIF, EPS, PNG, etc. Because of restrictions on the conversion from one format to the other, the use of the data in a specific format has been limited as well. Usually, the data size of multimedia is large such as video; therefore, multimedia data often require a large storage. Multimedia database consume a lot of processing time, as well as bandwidth [16]. Major Issues in MDM includes content based retrieval and similarity search which are integrated with mining methods, generalization and multidimensional analysis, classification and prediction analysis, and mining associations in multimedia data. The multi-dimensional approach makes navigating the database easier, screening for a particular subset of data, or asking for data in a particular way, and being able to define analytical calculations. The speed of these operations is much quicker and more consistent than in other database structures as the data is physically stored in a multi-dimensional structure [15]. Multimedia data mining needs content-based retrieval and similarity search integrated with mining methods. Content based retrieval in multimedia is a challenging problem since multimedia data needs detailed interpretation from pixel values [11].

Load more