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This paper presents a new approach of best background modeling for surveillance Information. The approach makes orthogonal non-separable wavelet transformation of information frames used for Background Modeling, extracts the approximate information to reconstruct information frames, filters out the disturbance, shadow and noise from the reconstructed frames, constructs basic background with the method of binary mask images, uses multi-frame combination of non-uniform noise to filter noise in basic background, applies mutual information to detect the situation of adjacent changes. If the background has a gradual change, weighted superposition of multi background modeling images with time will be applied to update the background. If the background has a major or sudden change, the background will remodel from this frame.

An .image indexing method is presented to perform the CBIR(Content Based Image Retrieval) for about 100 different images. The method is based on the fractal framework of the PIFS(Partitioned iterated function systems) widely used for image compression. The image indexing is represented through i-model features. A specific searching algorithm allows to use this representation as an indexing to retrieval an images. To optimize the matching and retrieval process, an indexed data structure is used to store the PIFS and to calculate the distance between the compressed images and the index image. Also, we present the theoretical arguments to justify their use in image retrieval.

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