14325657
An approach to reducing the labeling cost of Markov random fields within an infinite label space
This work proposes a novel idea, called SOIL, for reducing the computational complexity of the maximum a posteriori optimization problem using Markov random fields (MRFs). The local characteristics of MRFs are employed so that the searching in a virtually infinite label space is confined in a small finite space. Globally, the number of labels allowed is as many as the number of image sites while locally the optimal label is sampled from a space consisting of the labels assigned to the four-neighbor plus a random one. Neither the prior knowledge about the number of classes nor the estimation phase of the class number is required in this work. The proposed method is applied to the problem of texture segmentation and the result is compared with those obtained from conventional methods.
24590909
Fragile watermarking scheme exploiting non-deterministic block-wise dependency
Block-wise dependency is recognized as a key requirement in fragile watermarking schemes to thwart the vector quantization (VQ) attack. It has also been observed that deterministic dependency is still susceptible to transplantation attack or even simple cover-up attack. In this work, a new fragile watermarking scheme exploiting non-deterministic dependency is proposed. To watermark the original image, each pixel is adjusted according to the consistency between a key-dependent binary watermark bit, the information from the pixel itself and the non-deterministic secret dependency information calculated from a neighborhood centered at that pixel. Localization resolution is enhanced with a post-processing operation on the output difference map.
16331610
Calcification Descriptor and Relevance Feedback Learning Algorithms for Content-Based Mammogram Retrieval
In recent years a large number of digital mammograms have been generated in hospitals and breast screening centers. To assist diagnosis through indexing those mammogram databases, we proposed a content-based image retrieval framework along with a novel feature extraction technique for describing the degree of calcification phenomenon revealed in the mammograms and six relevance feedback learning algorithms, which fall in the category of query point movement, for improving system performance. The results show that the proposed system can reach a precision rate of 0.716 after five rounds of relevance feedback have been performed.
32219854
Unsupervised texture segmentation using multiresolution hybrid genetic algorithm
This work approaches the texture segmentation problem by incorporating genetic algorithm and k-mean clustering method within a multiresolution structure. First, a quad-tree structure is constructed and the input image is partition into blocks at different resolution levels. Texture features are then extracted from each block. Based on the texture features, a hybrid genetic algorithm is employed to perform the segmentation. The crossover operator of traditional genetic algorithm is replaced with k-means clustering method while the mutate and select operators are adopted. In the final step, the boundaries and the segmentation result of the current resolution level are propagated down to the next level to act as contextual constraints and the initial configuration of the next level, respectively.
23059132
Image authentication and integrity verification via content-based watermarks and a public key cryptosystem
A technique using the inherent feature map of the underlying image as the watermark is proposed in this work. First, the binary feature map is extracted as watermark and partitioned into blocks. Secondly, neighboring feature map blocks are blended and encrypted for insertion. On the receiver side, the feature map from the received image is extracted again and compared against the recovered watermark to verify the integrity and authenticity. In addition the capability of detecting geometric transformation, removal of original objects and addition of foreign objects, the proposed scheme is also capable of localizing tampering and detecting cropping without a priori knowledge about the image.
35008704
Image segmentation based on a multiresolution Bayesian framework
A multiresolution Markov random field (MMRF) approach to texture segmentation which makes use of both regional and boundary information is proposed in this work. The algorithm is designed to improve the efficiency and solve two problems: over-segmentation and under-segmentation. At each resolution level, regional information and boundary information between textured regions is extracted to describe each block using the multiresolution Fourier transform (MFT). The image is then modelled as a sequence of Markov random fields (MRFs) of varying resolution and a Gibbs sampling scheme is applied to label the constituent sites at each scale by seeking the configuration with minimal interaction energy. The interaction energy is defined as a function of the regional and boundary information. The constraints of region context, and boundary smoothness and connectivity are also encoded in the interaction energy. Once the algorithm converges at a given scale, the segmentation result is propagated down to the next resolution for further refinement.
1209241
Spectral Energy Minimization for Semi-supervised Learning
Data mining problems often involve a large amount of unlabeled data and there is often very limited known information on the dataset. In such scenario, semi-supervised learning can often improve classification performance by utilizing unlabeled data for learning. In this paper, we proposed a novel approach to semi-supervised learning as as an optimization of both the classification energy and cluster compactness energy in the unlabeled dataset. The resulting integer programming problem is relaxed by a semi-definite relaxation where efficient solution can be obtained. Furthermore, the spectral graph methods provide improved energy minimization via the incorporation of additional criteria. Results on UCI datasets show promising results.
2190827
Chosen-Plaintext Cryptanalysis of a Clipped-Neural-Network-Based Chaotic Cipher
At ISNN'04, a novel symmetric cipher was proposed, by combining a chaotic signal and a clipped neural network (CNN) for encryption. The present paper analyzes the security of this chaotic cipher against chosen-plaintext attacks, and points out that this cipher can be broken by a chosen-plaintext attack. Experimental analyses are given to support the feasibility of the proposed attack.
35216739
Image Smoothing Using Parametric Relaxation
Abstract A new image-smoothing algorithm which smooths the image while preserving discontinuities is developed. The method poses the smoothing problem as an estimation problem of the maximum a posteriori probability of a pixel having a certain gray level. A prior probability density function is assigned to each pixel from the raw data, which is an estimate of the gray-level uncertainty. The algorithm is an application of the random measure field in image processing. Contextual information from neighbors is used in refining the probability distribution of each pixel, which is a continuous version of the relaxation algorithm. The way of parametrizing the probability functions enables the derivation of an efficient iterative formula for updating the gray-level values.
39573011
The adaptive modulated wavelet transform image representation
In this paper, a new, called the adaptive modulated wavelet transform (AMWT) image representation is presented. One of the attractive features is that the informative instantaneous frequencies of images can be taken into account to improve the representation performance via adaptation of the modulating frequencies involved. The transform coefficients in both wavelet and modulated wavelet domains are uniformly quantized with several quantization levels. The computed peak signal-to-noise ratio values and entropies are used as rate distortion curves for performance comparison. Experimental results show that AMWT out performs wavelet transform for representing images containing textures with rapid variation in grays.
5215475
QED: a novel quaternary encoding to completely avoid re-labeling in XML updates
The method of assigning labels to the nodes of the XML tree is called a labeling scheme. Based on the labels only, both ordered and un-ordered queries can be processed without accessing the original XML file. One more important point for the labeling scheme is the label update cost in inserting or deleting a node into or from the XML tree. All the current labeling schemes have high update cost, therefore in this paper we propose a novel quaternary encoding approach for the labeling schemes. Based on this encoding approach, we need not re-label any existing nodes when the update is performed. Extensive experimental results on the XML datasets illustrate that our QED works much better than the existing labeling schemes on the label updates when considering either the number of nodes or the time for re-labeling.