14744188
Learning Class-Specific Edges for Object Detection and Segmentation
Recent research into recognizing object classes (such as humans, cows and hands) has made use of edge features to hypothesize and localize class instances. However, for the most part, these edge-based methods operate solely on the geometric shape of edges, treating them equally and ignoring the fact that for certain object classes, the appearance of the object on the “inside” of the edge may provide valuable recognition cues. 
 
We show how, for such object classes, small regions around edges can be used to classify the edge into object or non-object. This classifier may then be used to prune edges which are not relevant to the object class, and thereby improve the performance of subsequent processing. We demonstrate learning class specific edges for a number of object classes — oranges, bananas and bottles — under challenging scale and illumination variation. 
 
Because class-specific edge classification provides a low-level analysis of the image it may be integrated into any edge-based recognition strategy without significant change in the high-level algorithms. We illustrate its application to two algorithms: (i) chamfer matching for object detection, and (ii) modulating contrast terms in MRF based object-specific segmentation. We show that performance of both algorithms (matching and segmentation) is considerably improved by the class-specific edge labelling.
7854172
OBJ CUT
In this paper, we present a principled Bayesian method for detecting and segmenting instances of a particular object category within an image, providing a coherent methodology for combining top down and bottom up cues. The work draws together two powerful formulations: pictorial structures (PS) and Markov random fields (MRFs) both of which have efficient algorithms for their solution. The resulting combination, which we call the object category specific MRF, suggests a solution to the problem that has long dogged MRFs namely that they provide a poor prior for specific shapes. In contrast, our model provides a prior that is global across the image plane using the PS. We develop an efficient method, OBJ CUT, to obtain segmentations using this model. Novel aspects of this method include an efficient algorithm for sampling the PS model, and the observation that the expected log likelihood of the model can be increased by a single graph cut. Results are presented on two object categories, cows and horses. We compare our methods to the state of the art in object category specific image segmentation and demonstrate significant improvements.
2464851
Fast Memory-Efficient Generalized Belief Propagation
Generalized Belief Propagation (gbp) has proven to be a promising technique for performing inference on Markov random fields (mrfs). However, its heavy computational cost and large memory requirements have restricted its application to problems with small state spaces. We present methods for reducing both run time and storage needed by gbp for a large class of pairwise potentials of the mrf. Further, we show how the problem of subgraph matching can be formulated using this class of mrfs and thus, solved efficiently using our approach. Our results significantly outperform the state-of-the-art method. We also obtain excellent results for the related problem of matching pictorial structures for object recognition.
14151624
Solving Markov Random Fields using Second Order Cone Programming Relaxations
This paper presents a generic method for solvingMarkov random fields (MRF) by formulating the problem of MAP estimation as 0-1 quadratic programming (QP). Though in general solving MRFs is NP-hard, we propose a second order cone programming relaxation scheme which solves a closely related (convex) approximation. In terms of computational efficiency, our method significantly outperforms the semidefinite relaxations previously used whilst providing equally (or even more) accurate results. Unlike popular inference schemes such as Belief Propagation and Graph Cuts, convergence is guaranteed within a small number of iterations. Furthermore, we also present a method for greatly reducing the runtime and increasing the accuracy of our approach for a large and useful class of MRFs. We compare our approach with the state-of-the-art methods for subgraph matching and object recognition and demonstrate significant improvements.
5533650
The hierarchical cliques interconnection network
The fully connected network possesses extremely good topological, fault-tolerant, and embedding properties. However, due to its high degree, the fully connected network has not been an attractive candidate for building parallel computers. On the other hand, tree-based networks are popular as parallel computer networks, even though they suffer from poor fault-tolerance and embedding properties. The hierarchical cliques interconnection network described in this paper incorporates positive features of the fully connected network and the tree network. In other words, the hierarchical cliques possess such desirable properties as low diameter, low degree, self routing, versatile embedding, good fault-tolerance and strong resilience. Hierarchical cliques can efficiently embed most important networks and possess a scalable, modular structure. Further, by combining hierarchical cliques with fat trees congestion in the upper levels can be alleviated.
14476885
Energy-efficient caching and prefetching with data consistency in mobile distributed systems
Summary form only given. In mobile distributed systems, vital resources like battery power and wireless channel bandwidth impose significant challenges in ubiquitous information access. We propose a novel energy and bandwidth efficient data caching mechanism, called greedydual least utility (GD-LU), that enhances dynamic data availability while maintaining consistency. The proposed utility-based caching mechanism considers several characteristics of mobile distributed systems, such as connection-disconnection, mobility handoff, data update and user request patterns to achieve significant energy savings in mobile devices. Based on the utility function derived from an analytical model, we propose a cache replacement algorithm and a passive prefetching algorithm to cache and prefetch data objects. Our comprehensive simulation experiments demonstrate that the proposed mechanism achieves more than 10% energy saving and near-optimal performance tradeoff between access latency and energy consumption.
10875247
Measuring Parallelism in Computation-Intensive Scientific/Engineering Applications
Describes COMET, (concurrency measurement tool), a software tool for measuring parallelism in large scientific/engineering applications. The proposed tool measures the total parallelism present in programs, filtering out the effects of communication/synchronization delays, finite storage, limited number of processors, the policies for management of processors and storage, etc. Although an ideal machine that can exploit the total parallelism is not realizable, such measures would aid the calibration and design of various architectures/compilers. The proposed software tool accepts ordinary Fortran programs as input. Therefore, parallelism can be measured easily on many fairly big programs. Some measurements for parallelism obtained with the help of this tool are also reported. It is observed that the average parallelism in the chosen programs is in the range of 500-3500 Fortran statements executing concurrently in each clock cycle in an idealized environment. >
7847540
Electronic Multidimensional Auctions and the Role of Information Feedback
* Part of this work was done while the first author was visiting the IBM T.J. Watson Research Center in Hawthorne, NY, whose hospitality is gratefully acknowledged. Anant Jhingran and Sunil Noronha and especially Mike Rothkopf provided valuable feedback on earlier versions. However, any remaining errors are solely the responsibility of the authors. AbstractTraditionally, companies aiming to achieve competition among suppliers have used sealed bidding procedures in their sourcing processes. The advances in information technology and in particular the Internet now allow these companies to use different and more complex auction mechanisms. In particular multidimensional auctions are a natural extension of the standard sealed-bid auctions, but these auctions raise a whole host of issues that have been little investigated. In this article we focus on one of these issues, namely the role of information feedback given during the auction process. We describe various feedback policies and analyze the expected impact on the performance of the auction mechanism using the criteria of speed of convergence, allocative efficiency and Pareto optimality. This can help both researchers and practitioners in a more detailed and thorough analysis of electronic auctions.
14783291
A large-vocabulary continuous speech recognition system for Hindi
In this paper we present two new techniques that have been used to build a large-vocabulary continuous Hindi speech recognition system. We present a technique for fast bootstrapping of initial phone models of a new language. The training data for the new language is aligned using an existing speech recognition engine for another language. This aligned data is used to obtain the initial acoustic models for the phones of the new language. Following this approach requires less training data. We also present a technique for generating baseforms (phonetic spellings) for phonetic languages such as Hindi. As is inherent in phonetic languages, rules generally capture the mapping of spelling to phonemes very well. However, deep linguistic knowledge is required to write all possible rules, and there are some ambiguities in the language that are difficult to capture with rules. On the other hand, pure statistical techniques for base and generation require large amounts of training data that are not readily available. We propose a hybrid approach that combines rule-based and statistical approaches in a two-step fashion. We evaluate the performance of the proposed approaches through various phonetic classification and recognition experiments.