5808505
Bootstrapping SVM active learning by incorporating unlabelled images for image retrieval
The performance of image retrieval with SVM active learning is known to be poor when started with few labeled images only. In this paper, the problem is solved by incorporating the unlabelled images into the bootstrapping of the learning process. In this work, the initial SVM classifier is trained with the few labeled images and the unlabelled images randomly selected from the image database. Both theoretical analysis and experimental results show that by incorporating unlabelled images in the bootstrapping, the efficiency of SVM active learning can be improved, and thus improves the overall retrieval performance.
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.
15085849
A novel framework for SVM-based image retrieval on large databases
In this paper, a novel framework is proposed to deliver a fast, robust, and generally applicable SVM-based image retrieval for large databases. A quick test scheme is developed, and on-line kernel learning is employed to realize it after analyzing the relationship between them. Then an upper bound on maximum test scope is derived to speed up testing further. Also, the general applicability is well maintained because this framework does not need a kernel function and index structure to be pre-defined. Taking the advantages of this framework, more sophisticated SVM can be used to improve retrieval performance while keeping short response time. Experimental results on large image databases verify the effectiveness and efficiency of the proposed framework.
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
5315254
Efficient and Secure Authentication Protocols for Mobile VoIP Communications
Mobile Voice over IP (Mobile VoIP) allows mobile users to continuously talk with each others while roaming across different networks. In such wireless application, it is vulnerable to information security. In this paper, we present two efficient and secure authentication protocols for mobile VoIP services. In our approaches, we first develop a secure authentication protocol for mobile IP registration procedure. And then, based on this authenticated process, we extend the similar concept to a SIP-based VoIP service, and thus propose the second simple and secure communication protocol. For verifying our contributions, we demonstrate our proposed protocols secure and efficient: (1) by conducting security analysis on the aspects of data integrity, confidentiality, and several common network attacks, such as replay, man-in-the-middle; and (2) by conducting simulations and showing comparison results with other existing protocols, in terms of the communication and computation overheads imposed on the mobile node. All results point out that our protocols are superior to others.
17154425
Background Subtraction using Incremental Subspace Learning
Background modeling and subtraction is a basic component of many computer vision and video analysis applications. It has a critical impact on the performance of object tracking and activity analysis. In this paper, we propose an effective and adaptive background modeling and subtraction approach that can deal with dynamic scenes. The proposed approach uses a subspace learning method to model the background and the subspace is updated on-line with a sequential Karhunen-Loeve algorithm. A linear prediction model is also used to make the detection more robust. Experimental results show that the proposed approach is able to model the background and detect moving objects under various type of background scenarios with close to real-time performance.
201804
Analysis on Appearance Reasons of the Positive Feedback Traders
Positive feedback traders buy the asset when price is rising and sell it when price is falling. This type of speculation in short term causes the violent fluctuation for a given asset price, and make great influence to the stabilization of the financial market. The price reaction model under the influence of noise traders is established by considering the influence which the investment cost and information arrival rate make to the arbitrage and asset price. We analyze the reasons and conditions why positive feedback traders are born from quantitative analysis
14052879
Automatic image annotation and retrieval using subspace clustering algorithm
The development of technology generates huge amounts of non-textual information, such as images. An efficient image annotation and retrieval system is highly desired. Clustering algorithms make it possible to represent visual features of images with finite symbols. Based on this, many statistical models, which analyze correspondence between visual features and words and discover hidden semantics, have been published. These models improve the annotation and retrieval of large image databases. However, image data usually have a large number of dimensions. Traditional clustering algorithms assign equal weights to these dimensions, and become confounded in the process of dealing with these dimensions. In this paper, we propose a top-down, subspace clustering algorithm as a solution to this problem. For a given cluster, we determine relevant features based on histogram analysis and assign greater weight to relevant features as compared to less relevant features. We have implemented four different models to link visual tokens with keywords based on the clustering results of our clustering algorithm and K-means algorithm, and evaluated performance using precision, recall and correspondence accuracy using benchmark dataset. The results show that our algorithm is better than traditional ones for automatic image annotation and retrieval.
17681650
Cross-Layer Optimized Data Gathering in Wireless Multimedia Sensor Networks
Using multimedia sensor nodes can enhance the capability of wireless sensor networks (WSNs) for event description. In some scenarios, e.g., an erupting volcano, the WSNs are not deployed to work for long time. Instead, the WSNs aim to deliver continuous and reliable multimedia data as much as possible within an expected lifetime. In this paper, we focus on the efficient multimedia data gathering in WSNs within an expected lifetime. A distributed transmission Radius and data generation Rate Adjustment (RRA) scheme is proposed based on a cross-layer design, considering the interaction among physical, network and transport layers. Simulation results show that the proposed RRA strategy can effectively enhance the data gathering performance in wireless multimedia sensor networks (WMSNs) by dynamically adjusting the transmission radius of sensor nodes and the data generation rate of source nodes.