SKEDSOFT

Introduction: Association rules involving multimedia objects can be mined in image and video databases. At least three categories can be observed:

• Associations between image content and non image content features: A rule like “If at least 50% of the upper part of the picture is blue, then it is likely to represent sky” belongs to this category since it links the image content to the keyword sky.
• Associations among image contents that are not related to spatial relationships: A rule like “If a picture contains two blue squares, then it is likely to contain one red circle as well” belongs to this category since the associations are all regarding image contents.
• Associations among image contents related to spatial relationships: A rule like “If a red triangle is between two yellow squares, then it is likely a big oval-shaped object is underneath” belongs to this category since it associates objects in the image with spatial relationships.

To mine associations among multimedia objects, we can treat each image as a transaction and find frequently occurring patterns among different images. There are some subtle differences between mining association rules in multimedia databases versus in transaction databases. First, an image may contain multiple objects, each with many features such as color, shape, texture, keyword, and spatial location, so there could be many possible associations. In many cases, a feature may be considered as the same in two images at a certain level of resolution, but different at a finer resolution level. Therefore, it is essential to promote a progressive resolution refinement approach. That is, we can first mine frequently occurring patterns at a relatively rough resolution level, and then focus only on those that have passed the minimum support threshold when mining at a finer resolution level. This is because the patterns that are not frequent at a rough level cannot be frequent at finer resolution levels. Such a multiresolution mining strategy substantially reduces the overall data mining cost without loss of the quality and completeness of data mining results. This leads to an efficient methodology for mining frequent itemsets and associations in large multimedia databases.

Second, because a picture containing multiple recurrent objects is an important feature in image analysis, recurrence of the same objects should not be ignored in association analysis. For example, a picture containing two golden circles is treated quite differently from that containing only one. This is quite different from that in a transaction database, where the fact that a person buys one gallon of milk or two may often be treated the same as “buys milk.” Therefore, the definition of multimedia association and its measurements, such as support and confidence, should be adjusted accordingly.

Third, there often exist important spatial relationships among multimedia objects, such as above, beneath, between, nearby, left-of, and so on. These features are very useful for exploring object associations and correlations. Spatial relationships together with other content-based multimedia features, such as color, shape, texture, and keywords, may form interesting associations. Thus, spatial data mining methods and properties of to pological spatial relationships become important for multimedia mining.