16659414
Context based multiscale classification of images
This paper presents an algorithm to segment images into four classes: background, photograph, text and graph. There are two important aspects about the algorithm. The first is that the algorithm takes a multiscale approach, which adaptively classifies an image at different resolutions. The multiscale structure enables accurate classification at class boundaries as well as fast classification overall. The second is that the context information, which is accumulated in the process of classification, is used to improve the classification accuracy.
3117640
Asymptotic Performance of Vector Quantizers with a Perceptual Distortion Measure
Gersho's (1979) bounds on the asymptotic performance of vector quantizers are valid for vector distortions which are powers of the Euclidean norm. Yamada, Tazaki, and Gray (1980) generalized the results to distortion measures that are increasing functions of the norm of their argument. In both cases, the distortion is uniquely determined by the vector quantization error, i.e., the Euclidean difference between the original vector and the codeword into which it is quantized. We generalize these asymptotic bounds to input-weighted quadratic distortion measures and measures that are approximately output-weighted-quadratic when the distortion is small, a class of distortion measures often claimed to be perceptually meaningful. An approximation of the asymptotic distortion based on Gersho's conjecture is derived as well. We also consider the problem of source mismatch, where the quantizer is designed using a probability density different from the true source density. The resulting asymptotic performance in terms of distortion increase in decibels is shown to be linear in the relative entropy between the true and estimated probability densities.
13204199
A source coding approach to classification by vector quantization and the principle of minimum description length
An algorithm for supervised classification using vector quantization and entropy coding is presented. The classification rule is formed from a set of training data {(X/sub i/, Y/sub i/)}/sub i=1//sup n/, which are independent samples from a joint distribution P/sub XY/. Based on the principle of minimum description length (MDL), a statistical model that approximates the distribution P/sub XY/ ought to enable efficient coding of X and Y. On the other hand, we expect a system that encodes (X, Y) efficiently to provide ample information on the distribution P/sub XY/. This information can then be used to classify X, i.e., to predict the corresponding Y based on X. To encode both X and Y, a two-stage vector quantizer is applied to X and a Huffman code is formed for Y conditioned on each quantized value of X. The optimization of the encoder is equivalent to the design of a vector quantizer with an objective function reflecting the joint penalty of quantization error and misclassification rate. This vector quantizer provides an estimation of the conditional distribution of Y given X, which in turn yields an approximation to the Bayes classification rule. This algorithm, namely discriminant vector quantization (DVQ), is compared with learning vector quantization (LVQ) and CART/sup R/ on a number of data sets. DVQ outperforms the other two on several data sets. The relation between DVQ, density estimation, and regression is also discussed.
26279535
On Zador's entropy-constrained quantization theorem
Zador's classic result for the asymptotic high-rate behavior of entropy-constrained vector quantization is recast in a Lagrangian form which better matches the Lloyd algorithm used to optimize such quantizers. A proof that the result holds for a general class of distributions is sketched.
10105
Real-Time Computerized Annotation of Pictures
Developing effective methods for automated annotation of digital pictures continues to challenge computer scientists. The capability of annotating pictures by computers can lead to breakthroughs in a wide range of applications, including Web image search, online picture-sharing communities, and scientific experiments. In this work, the authors developed new optimization and estimation techniques to address two fundamental problems in machine learning. These new techniques serve as the basis for the automatic linguistic indexing of pictures - real time (ALIPR) system of fully automatic and high-speed annotation for online pictures. In particular, the D2-clustering method, in the same spirit as K-Means for vectors, is developed to group objects represented by bags of weighted vectors. Moreover, a generalized mixture modeling technique (kernel smoothing as a special case) for nonvector data is developed using the novel concept of hypothetical local mapping (HLM). ALIPR has been tested by thousands of pictures from an Internet photo-sharing site, unrelated to the source of those pictures used in the training process. Its performance has also been studied at an online demonstration site, where arbitrary users provide pictures of their choices and indicate the correctness of each annotation word. The experimental results show that a single computer processor can suggest annotation terms in real time and with good accuracy.
1149168
Unsupervised Multiresolution Segmentation for Images with Low Depth of Field
Unsupervised segmentation of images with low depth of field (DOF) is highly useful in various applications. This paper describes a novel multiresolution image segmentation algorithm for low DOF images. The algorithm is designed to separate a sharply focused object-of-interest from other foreground or background objects. The algorithm is fully automatic in that all parameters are image independent. A multi-scale approach based on high frequency wavelet coefficients and their statistics is used to perform context-dependent classification of individual blocks of the image. Unlike other edge-based approaches, our algorithm does not rely on the process of connecting object boundaries. The algorithm has achieved high accuracy when tested on more than 100 low DOF images, many with inhomogeneous foreground or background distractions. Compared with he state of the art algorithms, this new algorithm provides better accuracy at higher speed.
39140992
Compression of the layered depth image
A layered depth image (LDI) is a new popular representation and rendering method for objects with complex geometries. Similar to a two-dimensional (2-D) image, the LDI consists of an array of pixels. However, unlike the 2-D image, an LDI pixel has depth information, and there are multiple layers at a pixel location. We develop a novel LDI compression algorithm that handles the multiple layers and the depth coding. The algorithm records the number of LDI layers at each pixel location, and compresses LDI color and depth components separately. For LDI layer with sparse pixels, the data is aggregated and then encoded. An empirical rate-distortion model is used to optimally allocate bits among different components. Compared with the benchmark compression tools such as JPEG-2000 and MPEG-4, our scheme improves the compression performance significantly.
925345
Attribute-Based Access Control with Hidden Policies and Hidden Credentials
In an open environment such as the Internet, the decision to collaborate with a stranger (e.g., by granting access to a resource) is often based on the characteristics (rather than the identity) of the requester, via digital credentials: access is granted if Alice's credentials satisfy Bob's access policy. The literature contains many scenarios in which it is desirable to carry out such trust negotiations in a privacy-preserving manner, i.e., so as minimize the disclosure of credentials and/or of access policies. Elegant solutions were proposed for achieving various degrees of privacy-preservation through minimal disclosure. In this paper, we present protocols that protect both sensitive credentials and sensitive policies. That is, Alice gets the resource only if she satisfies the policy, Bob does not learn anything about Alice's credentials (not even whether Alice got access), and Alice learns neither Bob's policy structure nor which credentials caused her to gain access. Our protocols are efficient in terms of communication and in rounds of interaction
5637831
Policy-hiding access control in open environment
In trust management and attribute-based access control systems, access control decisions are based on the attributes (rather than the identity) of the requester: Access is granted if Alice's attributes in her certificates satisfy Bob's access control policy. In this paper, we develop a policy-hiding access control scheme that protects both sensitive attributes and sensitive policies. That is, Bob can decide whether Alice's certified attribute values satisfy Bob's policy, without Bob learning any other information about Alice's attribute values or Alice learning Bob's policy. To enable policy-hiding access control, we introduce the notion of certified input private policy evaluation. Our construction uses Yao's scrambled circuit protocol and two new techniques introduced in this paper. One novel technique is constructing circuits with uniform topology that can compute arbitrary functions in a family. The other technique is committed-integer based oblivious transfer.
40919560
MIMO Techniques in Cluster-Based Wireless Sensor Networks
Advances in microsensor and radio technology will enable small but smart sensors to be deployed for a wide range of environment monitoring applications. Most architectures of wireless sensor networks (WSN) are built as homogeneous networks, where all sensor nodes have the same structure. But it is not suitable for cluster-based WSN. By using multi-antenna sensor node (MASN) as the cluster head (CH), we import multi-input-multi-output (MIMO) and single-input-multi-output (SIMO) communication modes into WSN. In this network scenario, tremendous energy saving is possible when transmission range is larger than a given threshold. This paper motivates and describes the realization of the inhomogeneous network, special communication scheme and simulation of energy consumption and delay model of it.
2953723
Hierarchical land cover information retrieval in object-oriented remote sensing image databases with native queries
Classification and change detection of land cover types in the remotely sensed images is one of the major applications in remote sensing. This paper presents a hierarchical framework for land cover information storage and retrieval from object-oriented (OO) remote sensing image databases. Multi-spectral (band) remotely sensed images are classified by an optimized k-means clustering algorithm. The land cover maps are then decomposed and indexed with region quad-tree data structure stored in an OO database. Native queries (NQs), which use the semantics of the OO programming language for query composition, are developed to retrieve land cover distribution information and detect the changes of each land cover type at multi levels. A prototype system was implemented and the experiments were conducted on a time series Landsat Thematic Mapper (TM) images. The results show the effectiveness of the framework and the potentials in other remote sensing applications like urban planning and drought monitoring.