12856888
On the rate distortion function of Bernoulli Gaussian sequences
We study the rate distortion function of the Bernoulli-Gaussian random variable which can be used to model sparse signals. Both lower and upper bounds on the rate distortion function are given.We show that the bounds are almost tight in the low distortion regime for sparse signals. Interestingly, a naive coding scheme is near-optimal in this scenario.
12575767
Universal Quadratic Lower Bounds on Source Coding Error Exponents
We consider the problem of block-size selection to achieve a desired probability of error for universal source coding. While Baron, et al. (2004; 1973) studied this question for rates in the vicinity of entropy for known distributions using central-limit-theorem techniques, we are interested in all rates for unknown distributions and use error-exponent techniques. By adapting a technique of Gallager from the exercises of Gallager (1971), we derive a universal lower bound to the source-coding error exponent that depends only on the alphabet size and is quadratic in the gap to entropy.
7907486
Joint source-channel with side information coding error exponents
In this paper, we study the upper and the lower bounds on the joint source-channel coding error exponent with decoder side-information. The results in the paper are non-trivial extensions of the Csiszár's classical paper [6]. Unlike the joint source-channel coding result in [6], it is not obvious whether the lower bound and the upper bound are equivalent even if the channel coding error exponent is known. For a class of channels, including the symmetric channels, we apply a game-theoretic result to establish the existence of a saddle point and hence prove that the lower and upper bounds are the same if the channel coding error exponent is known. More interestingly, we show that encoder side-information does not increase the error exponents in this case.
9272685
Genetic algorithm for affine point pattern matching
Point pattern matching (PPM) is an important topic in the fields of computer vision and pattern recognition. According to if there exists a one to one mapping between the two point sets to be matched, PPM can be divided into the case of complete matching and the case of incomplete matching. According to if utilizing information other than 2-D image coordinates, PPM can be divided into labelled point-matching case and unlabelled point-matching case. Using partial Hausdorff distance, this paper presents a genetic algorithm (GA) based method to solve the incomplete unlabelled matching problem under general affine transformation. Since it successfully reduces the solution space of GA by constructing 'feature ellipses' of point sets, the method can achieve high computing efficiency and good matching results. Theoretical analysis and simulation results show that the new algorithm is very effective.
1424357
Lossless Coding for Distributed Streaming Sources
Distributed source coding is traditionally viewed in a block coding context wherein all source symbols are known in advance by the encoders. However, many modern applications to which distributed source coding ideas are applied, are better modeled as having streaming data. In a streaming setting, source symbol pairs are revealed to separate encoders in real time and need to be reconstructed at the decoder with subject to some tolerable end-to-end delay. In this paper, a causal sequential random binning encoder is introduced and paired with maximum likelihood (ML) and universal decoders. The latter uses a novel weighted empirical suffix entropy decoding rule. We derive a lower bounds on the error exponent with delay for each decoder. We also provide upper bounds for the special case of streaming with decoder side information and discuss when upper and lower bounds match. We show that both ML and universal decoders achieve the same (positive) error exponents for all rate pairs inside the Slepian-Wolf achievable rate region. The dominant error events in streaming are different from those in block-coding and result in different exponents. Because the sequential random binning scheme is also universal over delays, the resulting code eventually reconstructs every source symbol correctly with probability one.
2546960
The Source Coding Game With a Cheating Switcher
The problem of finding the rate-distortion function of an arbitrarily varying source (AVS) composed of a finite number of memoryless subsources is revisited. Berger's 1971 paper “The Source Coding Game” solves this problem when the adversary is allowed only strictly causal access to the subsource realizations. The case when the adversary has access to the subsource realizations non-causally is considered. This new rate-distortion function is determined to be the maximum of the rate-distortion function over a set of independent and identically distributed (IID) random variables that can be simulated by the adversary. The results are extended to allow for partial or noisy observations of subsource realizations. The model is further explored by attempting to find the rate-distortion function when the `adversary' is actually helpful. Finally, a bound is developed on the uniform continuity of the IID rate-distortion function for finite-alphabet sources. The bound is used to give a sufficient number of distributions that need to be sampled to compute the rate-distortion function of an AVS to within a desired accuracy. The bound is also used to give a rate of convergence for the estimate of the rate-distortion function for an unknown IID source.
18355867
Multi-granularity Visualization of Trajectory Clusters Using Sub-trajectory Clustering
With the surging of the requirements of location-based services, mining various interesting patterns from the spatial data becomes more and more important. In this paper, we propose an approach for visualizing the trajectory clustering results based on sub-trajectory clusters discovered from large-scale trajectory data. At first, we segment each trajectory into a set of sub-trajectories by detecting its corner points. And then, we choose Fréchet distance to compute the similarity between sub-trajectories, and use a density-based clustering method to cluster sub-trajectories and get an augmented order of the sub-trajectories. The visualization method can support multi-granularity views of the generated sub-trajectory clusters. Experiments have demonstrated the applicability and benefits of the proposed approach.
1776041
The design of distributed real-time video analytic system
Video has become an indispensable carrier of information and requires extremely computation expensive analysis to understand high level semantics. The computational difficulties in extracting embedded information and bridging the semantic gap present the major challenges in interoperability, scalability and real-time response of video analytic systems. In this paper, we propose a distributed scalable infrastructure VAP (Video Analytic Platform) for supporting real-time video stream analysis. In VAP, the application requirements are represented as a Directed Acrylic Graph (DAG), where nodes stand for video analysis computation modules and links show data flow and dependencies between nodes. VAP leverages UIMA (Unstructured Information Management Architecture) framework as the data flow control engine and multiple commodity databases as the storage and computation resources. The actual executions of video analysis computation modules have been pushed down into database engines to minimize the data movement cost. We initially choose video surveillance in retail store as a representative application domain. As a case study, a prototype system has been developed to achieve fundamental functions of real-time video surveillance, including video capture/store, human detection/tracking and customer shopping trajectory analysis.
18226410
Real-time tracking with multiple cues by set theoretic random search
Conventional treatment of visual tracking has been to optimize an objective function in a probabilistic framework. In this formulation, efficient algorithms employing simple prior distributions are usually insufficient to handle clutters (e.g., Kalman filter). On the other hand, distributions that are complex enough to incorporate all a priori knowledge can make the problem computationally intractable (e.g., particle filters (PF)). This paper proposes a new formulation of visual tracking where every piece of information, be it from a priori knowledge or observed data, is represented by a set in the solution space and the intersection of these sets, the feasibility set, represents all acceptable solutions. Based on this formulation, we propose an algorithm whose objective is to find a solution in the feasibility set. We show that this set theoretic tracking algorithm performs effective face tracking and is computationally more efficient than standard PF-based tracking.
27390874
Color Sectors and Edge Features for Content-Based Image Retrieval
We provide a new image retrieval model which integrates the spatial distributing information of colors and the shape features. The color features are represented by an amelioration of HSV color model: color sector. To describe the relative position and orientation of objects within the image, hue edges in the image, computed using Canny approach, are processed by Hough transform to generate the spatial histogram which is used as shape representation. To address matching of image, we define two global distance measures based on the accumulative distance and vector-based distance to compare the similarity of two images. Experimental results indicate that our approach provides higher retrieval rate than that of single feature (color), is a better tradeoff between high efficiency and reasonable precision.
782406
Data Compression