14347347
Multilevel video representation with application to keyframe extraction
Content-based video analysis calls for efficient video representation. In this paper, a novel multi-level representation of video is proposed based on the principle components derived from low-level visual features. It can characterize the video content from the coarse level to the fine level according to its intrinsic structure. This representation form provides a flexible scheme for video content analysis such as summarization, classification, and retrieval. A newly proposed subspace method, kernel based PCA, is explored to achieve this conveniently. The application in keyframe extraction is investigated to demonstrate the benefits of this representation.
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...
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
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.
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.
27385183
Standard Deviation and Intra Prediction Mode Based Adaptive Spatial Error Concealment (SEC) in H.264/AVC
Transmission of compressed video over error prone channels may result in packet losses or errors, which can significantly degrade the image quality. Therefore an error concealment scheme is applied at the video receiver side to mask the damaged video. Considering there are 3 types of MBs (Macro Blocks) in natural video frame, i.e., Textural MB, Edged MB, and Smooth MB, this paper proposes an adaptive spatial error concealment which can choose 3 different methods for these 3 different MBs. For criteria of choosing appropriate method, 2 factors are taken into consideration. Firstly, standard deviation of our proposed edge statistical model is exploited. Secondly, some new features of latest video compression standard H.264/AVC, i.e., intra prediction mode is also considered for criterion formulation. Compared with previous works, which are only based on deterministic measurement, proposed method achieves the best image recovery. Subjective and objective image quality evaluations in experiments confirmed this.
35597589
A Novel Genetic Algorithm Based on Cure Mechanism of Traditional Chinese Medicine
Enlightened by traditional Chinese medicine theory, a novel genetic algorithm (CMGA), which applies two types of treatment methods of “bu” and “xie” and dialectical treatment principle of traditional Chinese medicine theory to canonical GA, is proposed. The core of CMGA lies on constructing a cure operator, which is dynamically assembled with “bu” operation that replaces normal genes with eugenic genes and “xie” operation that replaces abnormal genes with normal genes. The main idea underlying CMGA is to give full play to the role of guidance function of knowledge to the evolutionary process through the cure operator. The simulation test of TSP shows that CMGA can restrain the degeneration and premature convergence phenomenon effectively during the evolutionary process while greatly increasing the convergence speed.
16065929
Adaptive reduced-rank LCMV beamforming algorithms based on joint iterative optimization of filters: Design and analysis
This paper presents reduced-rank linearly constrained minimum variance (LCMV) beamforming algorithms based on joint iterative optimization of filters. The proposed reduced-rank scheme is based on a constrained joint iterative optimization of filters according to the minimum variance criterion. The proposed optimization procedure adjusts the parameters of a projection matrix and an adaptive reduced-rank filter that operates at the output of the bank of filters. We describe LCMV expressions for the design of the projection matrix and the reduced-rank filter. We then describe stochastic gradient and develop recursive least-squares adaptive algorithms for their efficient implementation along with automatic rank selection techniques. An analysis of the stability and the convergence properties of the proposed algorithms is presented and semi-analytical expressions are derived for predicting their mean squared error (MSE) performance. Simulations for a beamforming application show that the proposed scheme and algorithms outperform in convergence and tracking the existing full-rank and reduced-rank algorithms while requiring comparable complexity.
31093950
Extended Password Recovery Attacks against APOP, SIP, and Digest Authentication
In this paper, we propose password recovery attacks against challenge-response authentication protocols. Our attacks use a message difference for a MD5 collision attack proposed in IEICE 2008. First, we show how to efficiently find a message pair that collides with the above message difference. Second, we show that a password used in authenticated post office protocol (APOP) can be recovered practically. We also show that the password recovery attack can be applied to a session initiation protocol (SIP) and digest authentication. Our attack can recover up to the first 31 password characters in a short time and up to the first 60 characters faster than the naive search method. We have implemented our attack and confirmed that 31 characters can be successfully recovered.
14258683
A framework for checking behavioral compatibility for component selection
Component selection and composition are the main issues in component-based development (CBD). Existing approaches suffer from the problem that the components retrieved usually do not exactly fit with other components in the system being developed. While formal methods can be used to describe and check semantic characteristics to better match components, there are practical limitations which restrict their adoption. In this paper, we propose a framework to support a semantic description and selection of components. Towards this we first introduce a Simple Component Interface Language (SCIL). SCIL files can be translated to a variety of models including those that have a formal basis. We report our experience with two specific tools, viz., Reactive Modules and Alloy with a view to using tools based on formal methods but without exposing the details of the tools