Image Classification Based on Two Dimensional Wavelet Packet Spectrum

We have developed a new algorithm for image classification based on two dimensional wavelet packet spectrum. Two dimensional wavelet packet is used for texture classification. Our method uses both color features and texture features the image.  Our Method uses both multiple types of object features and context within the image.  The generative phase normalizes the description length of images, which can have an arbitrary number of extracted features of each type. In the discriminative phase, a classifier learns which images, as represented by this fixed-length description, contain the target object. We have tested the approach by comparing it to several other approaches in the literature and by experimenting with several different data sets and combinations of features. Our results, using color, texture, and structure features, show a significant improvement over previously published results in image retrieval. Using salient region features, we are competitive with recent results in object recognition.