es as positioning accuracy and object recognition/image recognition/image resolution improves; automatic social metadata capture improves as voice recognition improves. Manual metadata input (text tagging) improves as speech recognition improves. However, the benefits of these improvements can only be realised provided that standardised descriptors have been agreed to provide the basis for interoperability. Interoperability in this context means the ability to capture, process and share metadata from multiple sources. 10.10.1 The Role of MPEG 75 in Mobile Metadata Standardisation Even with improved speech recognition, text-based metadata input is problematic in that it is nonstandardised. Each user will tend to use a different vocabulary and syntax. This makes it difficult to implement inference and similarity algorithms other than slightly haphazard word and phrase matching. It is easier to standardise automatically generated metadata. For example, to mandate pre- scriptive methods for describing the harmonic and temporal structures of a voice file or audio file or the colour, texture, shape and gradient of an image file or the structure and semantics (who is doing what to whom) of a video file. Images, video and audio files can usually use Fourier descriptors, given that the majority of image, video and audio content will have been compressed using Fourier-based JPEG 5 htp://mpeg.chiariglione.org/standards/mpeg-7/mpeg-7.htm#3.1_MPEG-7_Multimedia_Description_Schemes. Making Telecoms Work or MPEG compression algorithms. The purpose of MPEG7 is to standardise these audio and visual descriptors to allow the development of standardised search, match and retrieval techniques, including metadata-based inference and similarity algorithms. Taking imaging as an example, descriptors can be region specific or macroblock specific. For example, it is possible to search for a man in a red woolly hat in a particular area of an image in multiple images by looking for the frequency descriptor for the colour red and the 'closest 