Explain the categories of multimedia data mining.

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 CATEGORIES OF MULTIMEDIA DATA MINING

 Multimedia data mining is classified into two broad categories as static media and dynamic media. Static media contains text (digital library, creating SMS and MMS) and images (photos and medical images). Dynamic media contains Audio (music and MP3 sounds) and Video (movies). Multimedia mining refers to the analysis of a large amount of multimedia information in order to extract patterns based on their statistical relationships. Figure 1 shows the categories of multimedia data mining. 



Text mining
 Text Mining also referred to 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 a huge amount of usual
language text and it detects exact patterns to find useful information.

Image mining
Image mining systems can discover meaningful information or image patterns from a huge
collection of images. Image mining determines how low-level pixel representation consists of a
raw image or image sequence can be handled to recognize high-level spatial objects and
relationships. It includes digital image processing, image understanding, database, AI, and so
on. 

Video Mining
Video mining is unsubstantiated to find the interesting patterns from a large amount of video data;
multimedia data is video data such as text, image, and metadata, visual and audio. The processing
is 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 .

Audio mining
Audio mining plays an important role in multimedia applications, is a technique by which the
content of an audio signal can be automatically searched, analyzed, and rotten with wavelet
transformation. Band energy, frequency centroid, zero-crossing rate, pitch period and band-width
are often used features for audio processing . It is generally used in the field of automatic
speech recognition, where the analysis efforts to find any speech within the audio

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