31438585
Multiscale Similarity Matching for Subimage Queries of Arbitrary Size
Many image database management systems support whole-image queries. However, in some situations, users may only remember certain portions of the images. In this paper, we develop Padding and Reduction Algorithms to support subimage queries of arbitrary size based on local color information. The idea is to estimate the best-case lower bound to the distance between the query and the image. To improve the efficiency and effectiveness of content-based retrieval, a multiscale representation is proposed. Since image contents are usually pre-extracted and stored, the number of levels used in such a representation needs to be determined. We address this issue analytically by estimating the CPU and I/O costs, and experimentally by comparing the performance and accuracy of the outcomes of various filtering schemes. Our findings suggest that a 3-level hierarchy is preferred.
18562452
Interactive constrained frequent-pattern mining system
Data mining refers to the search for implicit, previously unknown, and potentially useful information (such as frequent patterns) that might be embedded in data. Most of the existing data mining algorithms do not allow users to express the patterns to be mined according to their intention via the use of constraints. Consequently, these unconstrained mining algorithms can yield numerous patterns that are not interesting to users. Moreover, data mining is supposed to be an exploratory process. In this context, we are working on a project with the objective of implementing an efficient, interactive, human-centered system for mining frequent patterns that satisfy the user constraints. We develop such a system, called iCFP, for interactive mining of constrained frequent patterns. Our developed system uses a tree-based mining framework. In addition, it (i) allows human users to impose a certain focus on the mining process, (ii) provides users with feedback during the mining process, and (iii) permits users to dynamically change their constraints during the process.
3110883
OSSM: a segmentation approach to optimize frequency counting
Computing the frequency of a pattern is one of the key operations in data mining algorithms. We describe a simple yet powerful way of speeding up any form of frequency counting satisfying the monotonicity condition. Our method, the optimized segment support map (OSSM), is a light-weight structure which partitions the collection of transactions into m segments, so as to reduce the number of candidate patterns that require frequency counting. We study the following problems: (1) what is the optimal number of segments to be used; and (2) given a user-determined m, what is the best segmentation/composition of the m segments? For Problem 1, we provide a thorough analysis and a theorem establishing the minimum value of m for which there is no accuracy lost in using the OSSM. For Problem 2, we develop various algorithms and heuristics, which efficiently generate OSSMs that are compact and effective, to help facilitate segmentation.
2108143
CanTree: a tree structure for efficient incremental mining of frequent patterns
Since its introduction, frequent-pattern mining has been the subject of numerous studies, including incremental updating. Many existing incremental mining algorithms are Apriori-based, which are not easily adoptable to FP-tree based frequent-pattern mining. In this paper, we propose a novel tree structure, called CanTree (canonical-order tree), that captures the content of the transaction database and orders tree nodes according to some canonical order. By exploiting its nice properties, the CanTree can be easily maintained when database transactions are inserted, deleted, and/or modified. For example, the CanTree does not require adjustment, merging, and/or splitting of tree nodes during maintenance. No rescan of the entire updated database or reconstruction of a new tree is needed for incremental updating. Experimental results show the effectiveness of our CanTree.
6383602
Efficient dynamic mining of constrained frequent sets
Data mining is supposed to be an iterative and exploratory process. In this context, we are working on a project with the overall objective of developing a practical computing environment for the human-centered exploratory mining of frequent sets. One critical component of such an environment is the support for the dynamic mining of constrained frequent sets of items. Constraints enable users to impose a certain focus on the mining process; dynamic means that, in the middle of the computation, users are able to (i) change (such as tighten or relax) the constraints and/or (ii) change the minimum support threshold, thus having a decisive influence on subsequent computations. In a real-life situation, the available buffer space may be limited, thus adding another complication to the problem.In this article, we develop an algorithm, called DCF, for Dynamic Constrained Frequent-set computation. This algorithm is enhanced with a few optimizations, exploiting a lightweight structure called a segment support map. It enables DCF to (i) obtain sharper bounds on the support of sets of items, and to (ii) better exploit properties of constraints. Furthermore, when handling dynamic changes to constraints, DCF relies on the concept of a delta member generating function, which generates precisely the sets of items that satisfy the new but not the old constraints. Our experimental results show the effectiveness of these enhancements.
7407086
Planning under uncertainty using model predictive control for information gathering
Abstract This paper considers trajectory planning problems for autonomous robots in information gathering tasks. The objective of the planning is to maximize the information gathered within a finite time horizon. It is assumed that either the Extended Kalman Filter (EKF) or the Extended Information Filter (EIF) is applied to estimate the features of interest and the information gathered is expressed by the covariance matrix or information matrix. It is shown that the planning process can be formulated as an optimal control problem for a nonlinear control system with a gradually identified model. This naturally leads to the Model Predictive Control (MPC) planning strategy, which uses the updated knowledge about the model to solve a finite horizon optimal control problem at each time step and only executes the first control action. The proposed MPC framework is demonstrated through solutions to two challenging information gathering tasks: (1) Simultaneous planning, localization, and map building (SPLAM) and (2) Multi-robot Geolocation. It is shown that MPC can effectively deal with dynamic constraints, multiple robots/features and a range of objective functions.
11422048
Towards Collaborative Travel Recommender Systems
Collaborative filtering (CF) based recommender systems have been proven to be a promising solution to the problem of information overload. Such systems provide personalized recommendations to users based on their previously expressed preferences and that of other similar users. In the past decade, they have been successfully applied in various domains, such as the recommendation of books and movies, where items are simple, independent and single units. When applied in the tourism domain, however, CF falls short due to the simplicity of existing techniques and complexity of tourism products. In view of this, a study was carried out to review the research problems and opportunities. This paper details the results of the study, which includes a review on the recent developments in CF as well as recommender systems in tourism, and suggests future research directions for personalized recommendation of tourist destinations and products.
32776525
Maximum Segmented Image Information Thresholding
Abstract Utilizing information theory and considering image segmentation from a communication perspective, the image segmentation process is interpreted as a data processing step that operates on a gray-scale image and produces a segmented image. It is shown that the segmented image contains a certain amount of information about the scene, which is defined as segmented image information (SII). It is proposed that the SII should be maximized when an image is thresholded, and this is known as the maximum segmented image information (MSII) thresholding criterion. The MSII thresholding criterion possesses better properties as compared with the minimum error (MINE) and the uniform error (UNFE) thresholding criteria. Based on the MSII thresholding criterion, an MSII thresholding algorithm is proposed for the thresholding of real images. The MSII thresholding algorithm is evaluated against several well-known thresholding algorithms. The good thresholding results of both synthetic and real images confirm the capabilities of the proposed MSII thresholding algorithm.
61028395
An energy minimisation approach to stereo-temporal dense reconstruction
We propose a novel energy minimisation framework for the dense reconstruction of stereo image sequences that incorporates data fidelity as well as spatial and temporal regularity. An iterated dynamic programming scheme is proposed to minimise the energy function. We also present an efficient implementation of the minimisation scheme by introducing morphological decomposition techniques to solve the dynamic programming subproblem. Our proposed method is capable of reconstructing dynamic scenes with complex motion. Results are presented demonstrating the strength of our proposed algorithm.
9755080
A 1.2V buck converter with a novel on-chip low-voltage current-sensing scheme
A low-voltage on-chip current-sensing circuit for buck power converter is proposed. The minimum supply voltage of the proposed current-sensing circuit is 1.2V (where V/sub th/ = 0.85V at room temperature), and the sensing accuracy is higher than 94%. The proposed buck converter is able to operate at a 1.2-V supply with higher than 98% power-conversion efficiency and with a maximum output current of 120mA.