9462236
PSDBoost: Matrix-Generation Linear Programming for Positive Semidefinite Matrices Learning
In this work, we consider the problem of learning a positive semidefinite matrix. The critical issue is how to preserve positive semidefiniteness during the course of learning. Our algorithm is mainly inspired by LPBoost [1] and the general greedy convex optimization framework of Zhang [2]. We demonstrate the essence of the algorithm, termed PSDBoost (positive semidefinite Boosting), by focusing on a few different applications in machine learning. The proposed PSDBoost algorithm extends traditional Boosting algorithms in that its parameter is a positive semidefinite matrix with trace being one instead of a classifier. PSDBoost is based on the observation that any trace-one positive semidefinite matrix can be decomposed into linear convex combinations of trace-one rank-one matrices, which serve as base learners of PSDBoost. Numerical experiments are presented.
16763535
Handling Significant Scale Difference for Object Retrieval in a Supermarket
We propose an object retrieval application which can retrieve user specified objects from a big supermarket. Significant and unpredictable scale difference between the query and the database image is the major obstacle encountered. The widely used local invariant features show their deficiency in such an occasion. To improve the situation, we first design a new weighting scheme which can assess the repeatability of local features against scale variance. Also, another method which deals with scale difference through retrieving a query under multiple scales is also developed. Our methods have been tested on a real image database collected from a local supermarket and outperform the existing local invariant feature based image retrieval approaches. A new spatial check method is also briefly discussed.
17672081
A Sub-Vector Weighting Scheme for Image Retrieval with Relevance Feedback
In image retrieval with relevance feedback, feature components are weighted to reflect the high-level concepts, and a user's subjective perception, embodied in the images labelled by the user in the feedback. However, the number of labelled images is often small and the covariance matrix needed for weighting will be singular. For this reason, the commonly used methods discard the mutual correlation among the feature components completely and use a diagonal covariance matrix. In this paper, a sub-vector weighting scheme is proposed. This scheme partitions a multi-dimensional visual feature vector into multiple low-dimensional sub-vectors. The singularity of the covariance matrix for each sub-vector can be avoided due to the lower dimensionality of the sub-vectors. Thus, the mutual correlation in each sub-vector can be retained for weighting and an optimally weighted similarity metric can be applied on each sub-vector. The similarity scores obtained from different sub-vectors are combined, as the final scor...
2447873
Multilabel SVM active learning for image classification
Image classification is an important task in computer vision. However, how to assign suitable labels to images is a subjective matter, especially when some images can be categorized into multiple classes simultaneously. Multilabel image classification focuses on the problem that each image can have one or multiple labels. It is known that manually labelling images is time-consuming and expensive. In order to reduce the human effort of labelling images, especially multilabel images, we proposed a multilabel SVM active learning method. We also proposed two selection strategies: Max Loss strategy and Mean Max Loss strategy. Experimental results on both artificial data and real-world images demonstrated the advantage of proposed method.
7983053
Auto-Calibration of a Compound-Type Omnidirectional Camera
We propose a method for auto-calibrating a compound-type omnidirectional camera (system). This camera produces spherical panoramic image through stitching some individual images captured by several modular cameras, each of which covers a different field of view of the full viewing sphere. By auto-calibration we mean that our algorithm works online in a self-calibration manner: it receives as input image point correspondences only, and outputs all the camera parameters. The calibration task consists of lens-distortion-removal, intrinsic-calibration, and relative-orientation. The focus of the paper is an algorithm for automatically removing lens distortion. By exploiting planar projective invariants, our algorithm is capable of estimating centre-of-distortion and distortion parameters efficiently without using any nonlinear iteration.
34620301
Video Local Pattern based Image Matching for Visual Mapping
Image matching plays an important role in visual mapping, a critical task of vision based mobile robot navigation. Based on the observation that the visual content in these video sequences generally changes in a slow and continuous mode, a concept of "video local pattern" is proposed to model each video frame, which is defined as a set of frames that are visually-similar and temporally-adjacent to that frame. Instead of manually labelling, a tracking based method is developed to automatically detect the local pattern for each frame. A model is then estimated from a local pattern, and the matching of images is performed by comparing these models. Experimental results demonstrated its improvement over that comparing individual frames
28662296
A Service Oriented Implementation of Distributed Status Monitoring and Fault Diagnosis Systems
The service-oriented architecture (SOA) can greatly improve the collaboration of devices in a distributed environment. With this architecture, human can be liberated from the burdensome configuration and management of distributed systems, especially when the nodes of the systems are dynamically changed. In this paper, we present a method to implement service-oriented distributed systems in industry. As a case study, we present a service-oriented Status Monitoring and Fault Diagnosis Systems (SMFDS) for large equipment. Our work is based on the UPnP (Universal Plug and Play) technology. Through abstracting the interfaces of devices and providing service middleware in an independent thread or process, the SOA can be easily implemented on an existing system with the least modifications. This characteristic is very useful when upgrading an existing system, for cost and risk consideration.
205393463
A new algorithm for solving classical Blasius equation
In this paper, a reliable algorithm is employed to investigate the classical Blasius equation. The algorithm is based mainly on applying the Adomian decomposition method to the transformation of the Blasius equation. The results demonstrate reliability and efficiency of the proposed algorithm.
18857320
Chinese Deterministic Dependency Parser Considering Prepositional Phrase
It's proved that Nivre’s algorithm causes some errors for determining the right-side dependents. In Chinese, only verbs and prepositions have right-side dependents. Jin proposed a two-phase parser to solve verbs' problems. In this paper, we present a method to solve prepositions 'problems. Experimental results show that our approach achieves higher accuracy than previous approach.
734146
Using Description Logics Reasoner for Ontology Matching
Ontology matching produces correspondences between elements of two ontologies and it is a basic problem in many database application domains. A Description Logics-based ontology matching approach is proposed. The approach achieves the matching by means of semantic interpretation. The algorithm of deciding ontology correspondences (such as equivalence, more general, less general, disjointness, overlapping) is presented, which is reasoned based on concept inclusion. The reasoner RacerPro is selected to deduce ontology mapping, and the experimental results demonstrate that the approach is feasible.
11915654
Hybrid Neural Network Architecture for On-Line Learning
Approaches to machine intelligence based on brain models use neural networks for generalization but they do so as signal processing black boxes. In reality, the brain consists of many modules that operate in parallel at different levels. In this paper we propose a more realistic biologically inspired hybrid neural network architecture that uses two kinds of neural networks simultaneously to consider short-term and long-term characteristics of the signal. The first of these networks quickly adapts to new modes of operation whereas the second one provides more accurate learning within a specific mode. We call these networks the surfacing and deep learning agents and show that this hybrid architecture performs complementary functions that improve the overall learning. The performance of the hybrid architecture has been compared with that of back-propagation perceptrons and the CC and FC networks for chaotic time-series prediction, the CATS benchmark test, and smooth function approximation. It is shown that the proposed architecture provides a superior performance based on the RMS error criterion.