5763461
AdaBoost with SVM-based component classifiers
The use of SVM (Support Vector Machine) as component classifier in AdaBoost may seem like going against the grain of the Boosting principle since SVM is not an easy classifier to train. Moreover, Wickramaratna et al. [2001. Performance degradation in boosting. In: Proceedings of the Second International Workshop on Multiple Classifier Systems, pp. 11-21] show that AdaBoost with strong component classifiers is not viable. In this paper, we shall show that AdaBoost incorporating properly designed RBFSVM (SVM with the RBF kernel) component classifiers, which we call AdaBoostSVM, can perform as well as SVM. Furthermore, the proposed AdaBoostSVM demonstrates better generalization performance than SVM on imbalanced classification problems. The key idea of AdaBoostSVM is that for the sequence of trained RBFSVM component classifiers, starting with large @s values (implying weak learning), the @s values are reduced progressively as the Boosting iteration proceeds. This effectively produces a set of RBFSVM component classifiers whose model parameters are adaptively different manifesting in better generalization as compared to AdaBoost approach with SVM component classifiers using a fixed (optimal) @s value. From benchmark data sets, we show that our AdaBoostSVM approach outperforms other AdaBoost approaches using component classifiers such as Decision Trees and Neural Networks. AdaBoostSVM can be seen as a proof of concept of the idea proposed in Valentini and Dietterich [2004. Bias-variance analysis of support vector machines for the development of SVM-based ensemble methods. Journal of Machine Learning Research 5, 725-775] that Adaboost with heterogeneous SVMs could work well. Moreover, we extend AdaBoostSVM to the Diverse AdaBoostSVM to address the reported accuracy/diversity dilemma of the original Adaboost. By designing parameter adjusting strategies, the distributions of accuracy and diversity over RBFSVM component classifiers are tuned to maintain a good balance between them and promising results have been obtained on benchmark data sets.
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.
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.
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
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.
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...
37162560
Fuzzy Control Strategy of Sub-mini Underwater Robots in Rectifying Control
14688887
Data Compression Algorithm Based on Hierarchical Cluster Model for Sensor Networks
A new distributed algorithm of data compression based on hierarchical cluster model for sensor networks is proposed, in which, the whole sensor network is mapped into a kind of hierarchical clusters model firstly, and then different wavelet transform models are used to commit data compression in inner and super clusters respectively. Theoretical analyses and simulation results show that, the above new methods have good performance of approximation, and can compress data and reduce the amount of data efficiently.
16913320
Continuity Properties of Equilibria in Some Fisher and Arrow-Debreu Market Models
Following up on the work of Megiddo and Vazirani [10], who determined continuity properties of equilibrium prices and allocations for perhaps the simplest market model, Fisher's linear case, we do the same for: 
 
 
Fisher's model with piecewise-linear, concave utilities 
Fisher's model with spending constraint utilities 
Arrow-Debreu's model with linear utilities 
Eisenberg-Gale markets.
6828357
Study on Traffic Information Fusion Algorithm Based on Support Vector Machines
Support vector machine (SVM) is a new sort of machine learning method based on structure risk minimization (SRM) principle, which has high generalization capability. Many problems with small samples, nonlinearity or high dimension in pattern recognition could be solved by the method. In this paper, the traffic data on freeway were taken as research objects and an information fusion algorithm based on SVM about freeway incident detection was proposed. A SVM was trained and tested using the data obtained from the simulation under the condition of incident and non-incident. Compared with the multi-layer feed forward neural network (MLF) algorithm trained with the same data, the simulation results showed that the SVM offers a lower misclassification rate, higher correct detection rate and lower false alarm, and it can improve the detection performance
25158433
Diagnosis of delay faults due to resistive bridges, delay variations and defects
In this paper, we present the first diagnosis algorithm for combinational circuit blocks that considers all combinations of multiple gate/wire delay variations/defects and any single resistive bridge. One key component of the proposed algorithm is a new path-oriented effect-cause procedure to identify all possible suspects of above type that might have caused the timing errors observed during test. The second key component is an efficient data structure to represent the suspects. The third key component is a new algorithm to analyze passing tests to vindicate some of the suspects identified. This algorithm exploits the newly identified concept of fixed-but-unknown delays, i.e., the fact that during the period of diagnosis the values of all delay parameters for every gate and wire in the particular circuit under test remain fixed, although at values unknown to us. The final set of suspects reported by the algorithm is guaranteed to contain all possible causes of the observed timing errors. Experimental results on benchmark circuits show the effectiveness of the proposed approach