Content-based image search has long been considered a difficult task. Making correct conjectures on the user intention (perception) based on the query images is a critical step in the content-based search. One key concept in this paper is how we find the user preferred low-level image characteristics from the multiple positive samples provided by the user. The second key concept is how we generate a set of consistent "pseudo images" when the user does not provide a sufficient number of samples. The notion of image feature stability is thus introduced. The third key concept is how we use negative images as pruning criterion. In realizing the preceding concepts, an image search scheme is developed using the weighted low-level image features. At the end, quantitative simulation results are used to show the effectiveness of these concepts.

A global motion parameter estimation method is proposed. The method can be used to segment an image sequence into regions of different moving objects. For any two pixels belonging to the same moving object, their associated global motion components have a fixed relationship from the projection geometry of camera imaging. Therefore, by examining the measured motion vectors we are able to group pixels into objects and, at the same time, identify some global motion information. In the presence of camera zoom, the object shape is distorted and conventional translational motion estimation may not yield accurate motion modeling. A deformable block motion estimation scheme is thus proposed to estimate the local motion of an object in this situation. Some simulation results are reported. For an artificially generated sequence containing only zoom activity, we find that the maximum estimation error in the zoom factor is about 2. 8 %. Rather good moving object segmentation results are obtained using the proposed object local motion estimation method after zoom extraction. The deformable block motion compensation is also seen to outperform conventional translational block motion compensation for video material containing zoom activity.