18193431
A framework of 2D Fisher discriminant analysis: application to face recognition with small number of training samples
A novel framework called 2D Fisher discriminant analysis (2D-FDA) is proposed to deal with the small sample size (SSS) problem in conventional one-dimensional linear discriminant analysis (1D-LDA). Different from the 1D-LDA based approaches, 2D-FDA is based on 2D image matrices rather than column vectors so the image matrix does not need to be transformed into a long vector before feature extraction. The advantage arising in this way is that the SSS problem does not exist any more because the between-class and within-class scatter matrices constructed in 2D-FDA are both of full-rank. This framework contains unilateral and bilateral 2D-FDA. It is applied to face recognition where only few training images exist for each subject. Both the unilateral and bilateral 2D-FDA achieve excellent performance on two public databases: ORL database and Yale face database B.
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...
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.
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.
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
21998790
NetTopo: Beyond Simulator and Visualizer for Wireless Sensor Networks
Deploying real WSN testbed provides a realistic testing environment, and allows users to get more accurate test results. However, deploying real testbed is highly constrained by the available budget when the test needs a large scale WSN environment. By leveraging the advantages of both simulators and real testbed, an approach that integrates simulation environment and testbed can effectively solve both scalability and accuracy issues. In this paper, we present NetTopo for providing both simulation and visualization functions to assist the investigation of algorithms in WSNs. Two case studies are described to prove the effectiveness of NetTopo.
33935023
Artificial Immune Strategies Improve the Security of Data Storage
A novel artificial immune strategies based data storage model, called AIS-DS, is proposed for dealing with the problem of resources sharing in a storage area network (SAN). Especially for the multi-user's tasks, this technology has some essential features for ensuring the security and privacy of information and/or data, because a SAN here can be regarded exclusive for each user with its own vaccines (a kind of special codes assigned for this user), and on the other hand, damage or interference to the disk or type to some extent in a local area of SAN, will not destroy the integrity of the saved data. Furthermore, with AIS-DS, the privacy of user's coded/decoded data is guaranteed even if the disk is physically handed by some other unwanted users.
36695959
Selective Ensemble Algorithms of Support Vector Machines Based on Constraint Projection
This paper proposes two novel ensemble algorithms for training support vector machines based on constraint projection technique and selective ensemble strategy. Firstly, projective matrices are determined upon randomly selected must-link and cannot-link constraint sets, with which original training samples are transformed into different representation spaces to train a group of base classifiers. Then, two selective ensemble techniques are used to learn the best weighting vector for combining them, namely genetic optimization and minimizing deviation errors respectively. Experiments on UCI datasets show that both proposed algorithms improve the generalization performance of support vector machines significantly, which are much better than classical ensemble algorithms, such as Bagging, Boosting, feature Bagging and LoBag.
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.
44863002
Comparative Study on Bionic Optimization Algorithms for Sewer Optimal Design
Sewer network as a necessary urban infrastructure plays an important role in people’s daily life. Conventional optimization techniques have significant limitations on solving the problems of sewer optimal design. Because as a high-dimensional discrete complex optimization problem, sewer optimal design is characterized by its discrete objective function and, as an integer discrete variable, its decision variable amount keeps the same pace with engineering scales. Over the last decade, various kinds of modern bionic optimization algorithms with their special advantages have been created and applied into sewer optimal design successfully. Based on previous studies, this paper analyses and compares the solution performances of Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Ant Colony Algorithms (ACA) from the three aspects respectively, they are convergence, speed and complexity of algorithm. The research result shows that compared with the other two algorithms, the ACA manifests its superiority for better convergence, satisfactory speed and relatively small algorithm complexity, which are very suitable for solving the problems of sewer optimal design.