44572255
Approximated Classification in Interactive Facial Image Retrieval
For databases of facial images, where each subject is depicted in only one or a few images, the query precision of interactive retrieval suffers from the problem of extremely small class sizes. A potential way to address this problem is to employ automatic even though imperfect classification on the images according to some high level concepts. In this paper we point out that significant improvement in terms of the occurrence of the first subject hit is feasible only when the classifiers are of sufficient accuracy. In this work Support Vector Machines (SVMs) are incorporated in order to obtain high accuracy for classifying the imbalanced data. We also propose an automatic method to choose the penalty factor of training error and the width parameter of the radial basis function used in training the SVM classifiers. More significant improvement in the speed of retrieval is feasible with small classes than with larger ones. The results of our experiments suggest that the first subject hit can be obtained two to five times faster for semantic classes such as “black persons” or “eyeglass-wearing persons”.
16679508
Regularized Neighborhood Component Analysis
Discriminative feature extraction is one of the fundamental problems in pattern recognition and signal processing. It was recently proposed that maximizing the class prediction by neighboring samples in the transformed space is an effective objective for learning a low-dimensional linear embedding of labeled data. The associated methods, Neighborhood Component Analysis (NCA) and Relevant Component Analysis (RCA), have been proven to be useful preprocessing techniques for discriminative information visualization and classification. We point out here that NCA and RCA are prone to overfitting and therefore regularization is required. NCA and RCA's failure for high-dimensional data is demonstrated in this paper by experiments in facial image processing. We also propose to incorporate a Gaussian prior into the NCA objective and obtain the Regularized Neighborhood Component Analysis (RNCA). The empirical results show that the generalization can be significantly enhanced by using the proposed regularization method.
40550352
Partial Relevance in Interactive Facial Image Retrieval
For databases of facial images, where each subject has only a few images, the query precision of interactive retrieval suffers from the problem of extremely small class sizes. A novel method is proposed to relieve this problem by applying partial relevance to the interactive retrieval. This work extends an existing content-based image retrieval system, PicSOM, by relaxing the relevance criterion in the early rounds of the retrieval. Moreover, we apply linear discriminant analysis as a preprocessing step before training the Self-Organizing Maps (SOMs) so that the resulting SOMs have stronger discriminative power. The results of simulated retrieval experiments suggest that for semantic classes such as “black persons” or “bearded persons” the first image which depicts the target subject can be obtained three to six times faster than by retrieval without the partial relevance.
6993641
A Fast Fixed-Point Algorithm for Two-Class Discriminative Feature Extraction
We propose a fast fixed-point algorithm to improve the Relevant Component Analysis (RCA) in two-class cases. Using an objective function that maximizes the predictive information, our method is able to extract more than one discriminative component of data for two-class problems, which cannot be accomplished by classical Fisher's discriminant analysis. After prewhitening the data, we apply Newton's optimization method which automatically chooses the learning rate in the iterative training of each component. The convergence of the iterative learning is quadratic, i.e. much faster than the linear optimization by gradient. methods. Empirical tests presented in the paper show that feature extraction using the new method resembles RCA for low-dimensional ionosphere data and significantly outperforms the latter in efficiency for high-dimensional facial image data.
47144462
The Second Futamura Projection for Type-Directed Partial Evaluation
A generating extension of a program specializes the program with respect to part of the input. Applying a partial evaluator to the program trivially yields a generating extension, but specializing the partial evaluator with respect to the program often yields a more efficient one. This specialization can be carried out by the partial evaluator itself; in this case, the process is known as the second Futamura projection.We derive an ML implementation of the second Futamura projection for Type-Directed Partial Evaluation (TDPE). Due to the differences between ‘traditional’, syntax-directed partial evaluation and TDPE, this derivation involves several conceptual and technical steps. These include a suitable formulation of the second Futamura projection and techniques for making TDPE amenable to self-application. In the context of the second Futamura projection, we also compare and relate TDPE with conventional off-line partial evaluation.We demonstrate our technique with several examples, including compiler generation for Tiny, a prototypical imperative language.
8920735
Partition-based decision heuristics for image computation using SAT and BDDs
Methods based on Boolean satisfiability (SAT) typically use a conjunctive normal form (CNF) representation of the Boolean formula, and exploit the structure of the given problem through use of various decision heuristics and implication methods. We propose a new decision heuristic based on separator-set induced partitioning of the underlying CNF graph. It targets those variables whose choice generates clause partitions with disjoint variable supports. This can potentially improve performance of SAT applications by decomposing the problem dynamically within the search. In the context of a recently proposed image computation method combining SAT and BDDs, this results in simpler BDD subproblems. We provide algorithms for CNF partitioning - one based on a clause-variable dependency matrix, and another based on standard hypergraph partitioning techniques, and also for the use of partitioning information in decision heuristics for SAT. The effectiveness of the proposed partition-based heuristic is shown with practical results for reachability analysis of benchmark sequential circuits.
36782214
Comparison of Point-, Line- and Boundary-Sampled Intercepts Inside a Circle or Sphere
Point-, line- or boundary-sampled intercepts may be measured inside a particle as a measure of particle size. Each intercept is primarily characterized by three geometric properties: length, location and orientation. Circle and sphere models are used in the present study to analyze these properties. The probability distribution function, probability density function, expectation and coefficient of variation for each of the properties were presented based on geometric probability and mathematical statistics. Such presentation would be helpful for potential users of stereology to better understand the concept of intercepts and implement stereological intercept measurement for estimation of particle sizes in practice.
361158
Efficient Distributed SAT and SAT-Based Distributed Bounded Model Checking
SAT-based Bounded Model Checking (BMC), though a robust and scalable verification approach, still is computationally intensive, requiring large memory and time. Interestingly, with the recent development of improved SAT solvers, it is frequently the memory limitation of a single server rather than time that becomes a bottleneck for doing deeper BMC search. Distributing computing requirements of BMC over a network of workstations can overcome the memory limitation of a single server, albeit at increased communication cost. In this paper, we present: a) a method for distributed-SAT over a network of workstations using a Master/Client model where each Client worsktation has an exclusive partition of the SAT problem and uses knowledge of partition topology to communicate with other Clients, b) a method for distributing SAT-based BMC using the distributed-SAT. For the sake of scalability, at no point in the BMC computation does a single workstation have all the information. We experimented on a network of heterogenous workstations interconnected with a standard Ethernet LAN. To illustrate, on an industrial design with 13K FFs and 0.5M gates, the non-disributed BMC on a single workstation (with 4 Gb memory) ran out of memroy after reaching a depth of 120; on the otherhand, our SAT-based distributed BMC over 5 similar workstations was able to go upto 323 steps with a communication overhead of only 30%.
5056026
Rate distortion optimization in the scalable video coding
This paper discusses the rate distortion optimization (RDO) issue in the scalable video coding. It is different from that in the non-scalable video coding because a scalable codec usually presents the decoded video within a certain range of the quality, frame rate and resolution. Therefore, this paper first analyzes the problem from a general model and gives a common description by the Lagrangian formula. Secondly, an algorithm is proposed to solve the above optimization problem at a specific scalable codec: the H.26L-based PFGS. At the same time, two analytic formulae are derived to ascertain the parameters /spl lambda/ in this algorithm. The experimental results show that the proposed algorithm significantly improves the coding efficiency of the H.26L-based PFGS. Further studies on this problem will be quite significant and challenging.