14334349
Learning Cascaded Reduced-Set SVMs Using Linear Programming
This paper proposes a simple and efficient detection framework that uses reduced-set kernels. We first describe our approach which reduces the number of kernels. A convex optimization method is used for calculating the reduced sets. Following this, we propose a method that optimally designs the cascade. Our experimental results indicate that our method minimizes complexity regarding the number of kernels in the cascaded structure while preserving the low error rates. Our algorithm generates the optimal weight of kernels for each cascade stage. This proposed algorithm achieves high detection-rates at low computational cost.
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.
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
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...
18692029
A CCI-Feedback-Aided Scheduling Technique for MU-MIMO
Imperfect channel state information (CSI) at the transmitter is one of the major challenges that the downlink multiuser multiple-input-multiple-output (MU-MIMO) technology faces in real applications. It will lead to increased co- channel interference (CCI) among users and deteriorate the capacity of MU-MIMO seriously, especially at high signal-to- noise-ratios (SNRs). In this paper, we propose an advanced MU-MIMO scheduling technique to improve the capacity of MU-MIMO in the case of imperfect CSI at the transmitter (CSIT). The rational behind the proposed scheme is the observation that the CCI level increases with the number of users simultaneously supported in MU-MIMO transmission. Thus, decreasing the number of simultaneous users can reduce CCI and sometimes even increase the sum capacity. Motivated by this observation, we design a CCI-feedback-aided scheduling technique, which adaptively adjusts the number of simultaneous users based on the CCI measurement at the user side. Simulation results have shown that considerable capacity gain can be achieved by the proposed scheduling technique at the cost of only one bit additional feedback per user and marginal complexity increase at both the transmitter and user side.
26330758
Music Genre Classification Based on Multiple Classifier Fusion
Although researchers have made great progresses on music genre classification in recent years, the need for more accurate system is still not satisfied. In this paper, we propose a method for further reducing the classification error rate based on multiple classifier fusion. First of all, MFCCs and four features from MPEG-7 audio descriptor are extracted in every short time frame, and then a group of frames are gathered into a longer segment, in which mean and variance of these short time frames features are calculated. The segment is considered as the basic unit for training and testing module. Then random forest (RF) and multilayer perceptron neural network (MLP) are executed on such segment independently. Finally, a weighted voting fusion strategy is employed to fusion the result of the two classifiers on each segment, and the whole file decision is made by selecting the most frequently labeled genre over all the segments. Experiments showed that the approach is effective. The fusion result gets 12.4% relative reduction in error rate compared to our baseline system.
1252619
A fast approximate algorithm for training L1-SVMs in primal space
We propose a novel and fast algorithm to train support vector machines (SVMs) in primal space, which solves an approximate optimization of SVMs with the properties of unconstraint, continuity and twice differentiability by utilizing the Newton optimization technique. Further, we devise a special pre-extracting procedure to speed up the convergence of the algorithm by resorting to a high-quality initial solution. Theoretical studies show that the proposed algorithm produces an @?-approximate solution to standard SVMs and maintains low computational complexity. Experimental results on benchmark data sets demonstrate that our algorithm is much faster than the dual based method such as SVM^l^i^g^h^t while it achieves the similar test accuracy.
2723209
An energy-efficient skew compensation technique for high-speed skew-sensitive signaling
Presented in this paper is an energy-efficient skew compensation technique referred to as the resistance-based distributed clock deskew (RDSK) technique. In comparison with the existing clock deskew techniques, the proposed RDSK technique achieves significant power reduction while maintaining effective in skew management. Furthermore, the proposed RDSK provides a better scalability for future technology scaling. Simulation results of an on-chip interconnect communication system with distributed receivers designed in a 0.10 /spl mu/m CMOS process demonstrate 20%-51% power reduction over the existing techniques. The proposed RDSK is an essential element of our adaptive timing framework where circuit performance can be adjusted adaptively in response to time-varying disturbances and uncertainties.
7144886
Khatri-Rao Unitary Space-Time Modulation
Based on the Khatri-Rao matrix product, we propose a novel unitary space-time modulation design called KR-USTM in this paper. Different from existing USTM schemes, such as the systematic approach and space-time frequency-shift keying (ST-FSK), KR-USTM does not require any computer search and can be applied to any number of transmit antennas. Moreover, the special structure of KR-USTM also makes it a high-rate scheme and achieve full antenna diversity as well as lower decoding complexity. Simulation results show that the proposed KR-USTM constellation achieves error performance comparable to existing USTM designs at low rates, while it outperforms them at high rates.