Query by content, or content-based retri has recently been proposed as

an alternative to text-based retri for media such as images, video and audio.

Text-based retri is no longer appropriate for indexing such media, for several

reasons. Firstly, keyword annotation is labor intensive, and it is not even possible

when large sets of images are to be indexed. Secondly, these annotations are

drawn a predefined set of keywords which cannot cover all possible concepts

images may represent. Finally, keywords assignment is subjective to the person

making it. Therefore, content-based image retri (CBIR) systems propose to

index the media documents based on features extracted their content rather

than by textual annotations. For still images, these features can be color, shape,

texture, objects layout, edge direction, etc.-Query by content, or content-based retri　has recently been proposed as

an alternative to text-based retri　for media such as images, video and audio.

Text-based retri　is no longer appropriate for indexing such media, for several

reasons. Firstly, keyword annotation is labor intensive, and it is not even possible

when large sets of images are to be indexed. Secondly, these annotations are

drawn　a predefined set of keywords which cannot cover all possible concepts

images may represent. Finally, keywords assignment is subjective to the person

making it. Therefore, content-based image retri　(CBIR) systems propose to

index the media documents based on features extracted　their content rather

than by textual annotations. For still images, these features can be color, shape,

texture, objects layout, edge direction, etc.