Active contour models are effective image segmentation methods. However, they are very time‐consuming, and their convergence depends upon the choice of initial contour. To overcome the two drawbacks, in the study, the authors suggest a signal‐walking‐driven active contour model. By walking a signal, they construct a forest of object evolution. Each tree grows from a root object, and child node contains its shrunk or/and split version. The merit value of an object is a composite metric from the colour, edge, or/and shape properties. The merit function plays an important role in tree construction and the goodness of object evolution. The objects are selected and added to the tree in the levels the merit function reaches the local maxima. After the forest of object evolution is constructed, by traversing each tree branch in post‐order, the objects corresponding to maximum merit values are extracted as the final segmentation. Experimental results on a set of oil‐sand images indicate the proposed signal‐walking‐driven active contour model outperforms Chan and Vese's model and adaptive thresholding.

This paper presents a novel, effective, and efficient characterization of wavelet subbands by bit-plane extractions. Each bit plane is associated with a probability that represents the frequency of 1-bit occurrence, and the concatenation of all the bit-plane probabilities forms our new image signature. Such a signature can be extracted directly from the code-block code-stream, rather than from the de-quantized wavelet coefficients, making our method particularly adaptable for image retrieval in the compression domain such as JPEG2000 format images. Our signatures have smaller storage requirement and lower computational complexity, and yet, experimental results on texture image retrieval show that our proposed signatures are much more cost effective to current state-of-the-art methods including the generalized Gaussian density signatures and histogram signatures.

The fine particles of mineral ore are a mixture of granules of various sizes, which collectively look like a texture. It is difficult to estimate the size distribution of such fine particles using conventional image segmentation techniques. In this paper, we propose an image-retrieval-like approach to estimate the size distribution of the fine particles. We introduce a new wavelet signature and a fitting algorithm for wavelet coefficients based on the theory of fragmentation.

Image indexing and retrieval techniques are important for efficient management of visual databases. These techniques are generally developed based on the associated compression techniques. In the fractal domain, luminance offset and contrast scaling parameter are typically used as the fractal indices. However, luminance offset and contrast scaling parameter are strongly correlated. In this paper, we prove that range block mean and contrast scaling parameters are independent. Based on this independence, we propose four statistical indices for efficient image retrieval. In addition, we propose an efficient hierarchical indexing strategy based on the de and ac component analysis. Experimental results on a database of 416 texture images, created by decomposing 26 images, indicate that the proposed indices significantly improve the retrieval rate, compared to other retrieval methods.

The paper describes a novel watermarking method to hide a binary watermark in image files compressed by fractal block coding. The watermarking method utilizes a special type of orthogonalization fractal coding method where the fractal affine transform is determined by range block mean and contrast scaling. Such orthogonalization fractal decoding has a unique mean-invariant property for each range block where the mean of a range block of the decoding series is invariant. The proposed watermark embedding procedure inserts an m-sequence into the quantized code of the range block means. The watermark detection is statistically determined by computing the correlation coefficient. Experimental results show that the watermark is robust against image processing operations such as JPEG compression, low-pass filtering, rescaling and clipping.

The model coefficients for the case of the non-orthogonal basis in Jacquin's mappings are contrast scaling and luminance offset. After orthogonalization, the model coefficients become range block mean and contrast scaling. These two fractal coding algorithms have the same encoding procedure except that luminance offset is replaced by range block mean, however, their decoding algorithms are different. In this paper, we prove that the orthogonal decoding algorithm converges faster than the non-orthogonal algorithm, while the two decoding algorithms produce the same iteration series if the initial image is the range-averaged image and the step size equals the side-length of the range block.

A texture image can be characterized by its fractal feature, and hence, the fractal features can be used as the texture signature to retrieve the images. In this paper, based on the independence of fractal parameters, we improve the three-dimensional histogram into a composite index, in order to reduce computational complexity. Furthermore, under the same computational cost, we make the comparison between the proposed composite index and fractal-dimension-related features, and experimental results on a database of 640 texture images indicate that the proposed composite index outperforms fractal-dimension-related features

Image indexing and retrieval techniques are important for efficient management of visual databases. These techniques are generally developed based on the associated compression techniques. In the fractal domain, the luminance offset and contrast scaling parameters are typically used as the fractal index. We propose to use the range block mean and contrast scaling as the fractal index. The image retrieval is performed in two steps. First, a coarse search is performed using the histogram of the range block means. Subsequently, a fine search is performed using the 2D joint histogram of the range block mean and contrast scaling parameters. Experimental results on a database of 416 texture images indicate that the proposed indices significantly improve the retrieval rate, compared to other retrieval methods.

A new fractal decoding algorithm based on range block mean and contrast scaling is presented. As a result, the decoding image is represented as the sum of the DC and AC components. We prove that new decoding algorithm converges faster than existing ones. Experiments show that the new decoding algorithm converges at least 2-3 times faster than existing methods.

An image can be characterized by its fractal parameters, and hence, the fractal parameters can be used as the image signature to retrieve the images. In this paper, based on the principle that fractal transform is completely determined by luminance offset and contrast scaling, we first propose histogram of luminance offset as a statistical index, and we further propose three composite indices by combining individual histograms to enhance retrieval rate and reduce computational complexity. Experimental results on a database of 416 texture images indicate that the proposed indices significantly improve the retrieval rate, compared to other retrieval methods.

This paper presents a new adaptive search approach to reduce the computational complexity of fractal encoding. A simple but very efficient adaptive necessary condition is introduced to exclude a large number of unqualified domain blocks so as to speed-up fractal image compression. Furthermore, we analyzed an unconventional affine parameter that has better properties than the conventional luminance offset. Specifically, we formulated an optimal bit allocation scheme for the simultaneous quantizations of the usual scaling and the aforementioned unconventional affine parameter. Experiments on standard images showed that our adaptive search method yields superior performance over conventional fractal encoding.