10911475
Stretching Bayesian Learning in the Relevance Feedback of Image Retrieval
This paper is about the work on user relevance feedback in image retrieval. We take this problem as a standard two-class pattern classification problem aiming at refining the retrieval precision by learning through the user relevance feedback data. However, we have investigated the problem by noting two important unique characteristics of the problem: small sample collection and asymmetric sample distributions between positive and negative samples. We have developed a novel approach to stretching Bayesian learning to solve for this problem by explicitly exploiting the two unique characteristics, which is the methodology of BAyesian Learning in Asymmetric and Small sample collections, thus called BALAS. Different learning strategies are used for positive and negative sample collections in BALAS, respectively, based on the two unique characteristics. By defining the relevancy confidence as the relevant posterior probability, we have developed an integrated ranking scheme in BALAS which complementarily combines the subjective relevancy confidence and the objective feature-based distance measure to capture the overall retrieval semantics. The experimental evaluations have confirmed the rationale of the proposed ranking scheme, and have also demonstrated that BALAS is superior to an existing relevance feedback method in the current literature in capturing the overall retrieval semantics.
1852867
Semantic repository modeling in image database
This work is about content based image database retrieval, focusing on developing a classification based methodology to address semantics-intensive image retrieval. With self organization map based image feature grouping, a visual dictionary is created for color, texture, and shape feature attributes, respectively. Labeling each training image with the keywords in the visual dictionary, a classification tree is built. Based on the statistical properties of the feature space we define a structure, called /spl alpha/-semantics graph, to discover the hidden semantic relationships among the semantic repositories embodied in the image database. With the /spl alpha/-semantics graph, each semantic repository is modeled as a unique fuzzy set to explicitly address the semantic uncertainty and the semantic overlap existing among the repositories in the feature space. A retrieval algorithm combining the classification tree with the fuzzy set models to deliver semantically relevant image retrieval is provided. The experimental evaluations have demonstrated that the proposed approach models the semantic relationships effectively and outperforms a state-of-the-art content based image retrieval system in the literature both in effectiveness and efficiency.
5416143
A clustering based approach to efficient image retrieval
This paper addresses the issue of effective and efficient content based image retrieval by presenting a novel indexing and retrieval methodology that integrates color, texture, and shape information for the indexing and retrieval, and applies these features in regions obtained through unsupervised segmentation, as opposed to applying them to the whole image domain. In order to address the typical color feature "inaccuracy" problem in the literature, fuzzy logic is applied to the traditional color histogram to solve for the problem to a certain degree. The similarity is defined through a balanced combination between global and regional similarity measures incorporating all the features. In order to further improve the retrieval efficiency, a secondary clustering technique is developed and employed to significantly save query processing time without compromising the retrieval precision. An implemented prototype system has demonstrated a promising retrieval performance for a test database containing 2000 general-purpose color images, as compared with its peer systems in the literature.
6205131
Energy efficient multi-hop polling in clusters of two-layered heterogeneous sensor networks
In this paper we study two-layered heterogeneous sensor networks where two types of nodes are deployed in the network: basic sensor nodes and cluster head nodes. Basic sensor nodes are simple and inexpensive, while cluster head nodes are much powerful and much richer in energy. A cluster head node organizes the basic sensor nodes around it into a cluster. A basic sensor node does data collections and sends the data packets when polled by the cluster head. By introducing hierarchy, such a two-layered heterogeneous sensor network has better scalability than homogeneous sensor networks. It also has a smaller overall cost since networking functionalities are shifted from sensors to the cluster head. It also has a longer life time, as sensors send packets only when polled by the cluster head and less energy is consumed in collisions and idle listening. This type of network will be ideally suited for applications such as environmental monitoring. In this paper, we focus on finding energy efficient and collision-free polling schedules in the multi-hop cluster. To reduce energy consumption in idle listening, a schedule is optimal if it uses minimum time. We show that the problem of finding an optimal schedule is NP-hard, and then give a fast on-line algorithm. We also consider dividing a cluster into sectors to further reduce the idle listening time of sensors. We conducted simulations on the NS-2 simulator, and the results show that our polling scheme can reduce the active time of sensors by a significant amount while sustaining 100% throughput.
13915860
Object detection in aerial imagery based on enhanced semi-supervised learning
Object detection in aerial imagery has been well studied in computer vision for years. However, given the complexity of large variations of the appearance of the object and the background in a typical aerial image, a robust and efficient detection is still considered as an open and challenging problem. In this paper, we present the enhanced semi-supervised learning (ESL) framework and apply this framework to revising an object detection methodology we have developed in a previous effort. Theoretic analysis and experimental evaluation using the UCI machine learning repository clearly indicate the superiority of the ESL framework. The performance evaluations of the revised object detection methodology against the original one clearly demonstrate the promise and superiority of this approach
12558561
Statistical Analysis of Generalized Processor Sharing Scheduling Discipline
In this paper, we consider the problem of providing statistical guarantees (for example, on the tail distribution of delay) under the Generalized Processor Sharing (GPS) scheduling discipline. This work is motivated by, and is an extension of, Parekh and Gallager's deterministic study of GPS scheduling discipline with leaky-bucket token controlled sessions [PG93a,b, Parekh92]. Using the exponentially bounded burstiness (E.B.B.) process model introduced in [YaSi93a] as a source traffic characterization, we establish results that extend the deterministic study of GPS: for a single GPS server in isolation, we present statistical bounds on the tail distributions of backlog and delay for each session. In the network setting, we show that networks belonging to a broad class of GPS assignments, the so-called Consistent Relative Session Treatment (CRST) GPS assignments, are stable in a stochastic sense. In particular, we establish simple bounds on the tail distribution of backlog and delay for each session in a Rate Proportional Processor Sharing (RPPS) GPS network with arbitrary topology.
15502339
Optimal parallel scheduling algorithm for WDM optical interconnects with recirculating buffering
We study scheduling algorithms in WDM optical interconnects with recirculating buffering. The interconnect we consider has wavelength conversion capabilities. We focus on limited range wavelength conversion while considering full range wavelength conversion as a special case. We formalize the problem of maximizing throughput and minimizing packet delay in such an interconnect as a matching problem in a bipartite graph and give an optimal parallel algorithm that runs in O(Bk/sup 2/), as compared to O((N+B)/sup 3/k/sup 3/) time if directly applying other existing matching algorithms, where N is the number of input/output fibers, B is the number of fiber delay lines and k is the number of wavelengths per fiber.
383050
A Domain Decomposition Method for Fast Manifold Learning
We propose a fast manifold learning algorithm based on the methodology of domain decomposition. Starting with the set of sample points partitioned into two subdomains, we develop the solution of the interface problem that can glue the embeddings on the two subdomains into an embedding on the whole domain. We provide a detailed analysis to assess the errors produced by the gluing process using matrix perturbation theory. Numerical examples are given to illustrate the efficiency and effectiveness of the proposed methods.