62767012
Incorporating prior knowledge into SVM for image retrieval
SVM based image retrieval suffers from the scarcity of labelled samples. In this paper, this problem is solved by incorporating prior knowledge into SVM. Firstly, some prior knowledge of image retrieval is discussed and constructed. After that, the knowledge is incorporated into SVM optimization as a constraint, and a new knowledge-based target function is formulated. Based on this, a framework of image retrieval with knowledge based SVM is proposed. Experimental results demonstrate that the proposed method can effectively improve the learning and retrieval performance of SVM, especially when the number of labelled samples is small.
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...
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.
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
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
10848491
Detecting and Eliminating Potential Violations of Sequential Consistency for Concurrent C/C++ Programs
When a concurrent shared-memory program written with a sequential consistency (SC) model is run on a machine implemented with a relaxed consistency (RC) model, it could cause SC violations that are very hard to debug. To avoid such violations, programmers need to provide explicit synchronizations or insert fence instructions. In this paper, we propose a scheme to detect and eliminate potential SC violations by combining Shasha/Snir's conflict graph and delay set theory with existing data race detection techniques. For each execution, we generate a race graph, which contains the improperly synchronized conflict accesses, called race accesses, and race cycles formed with those accesses. As a race cycle would probably lead to a non-sequential-consistent execution, we call it a potential violation of sequential consistency (PVSC) bug. We then compute the race delays of race cycles, and suggest programmers to insert fences into source code to eliminate PVSC bugs. We further convert a race graph into a PC race graph, and improves cycle detection and race delay computation to O(n^2), where n is the number of race access instructions. We evaluate our approach with the SPLASH-2 benchmarks, two large real-world applications (MySQL and Apache), and several multi-threaded Cilk programs. The results show that (1) the proposed approach could effec-tively detect PVSC bugs in real-world applications with good scalability; (2) it retains most of the performance of the concurrent program after inserting required fence instructions, with less than 6.3% performance loss; and (3) the additional cost of our approach over traditional race detection techniques is quite low, with 3.3% on average.
32712174
Hailstone Detection Based on Image Mining
Hailstone is one of the main meteorological disasters and it is very difficult to be forecasted effectively. A novel method of automatic recognition hail cloud is proposed base on association rules in this paper. Firstly, the method preprocesses the original reflectivity image. Then the color features and texture features are extracted from the radar echo image. Lastly, the association rules, which are used to distinguish hailstorm from rainstorm and super refract, are constructed, namely hail echo ensemble forecast model based on image mining is constructed. The results show that hail echo ensemble forecast model based on image mining has higher accuracy compared with the PUP products.
14183415
Context-aware application programming for mobile devices
This research aims at facilitating the development of context-aware application software for mobile devices by providing a programming model, an implementation framework and a development environment. The programming model provides a multi-layered software architecture for context-aware application programming. The model supports developers to define contexts, behaviors and context-behavior binding rules through specifications and automates generation of context-aware application code based on the specifications. The implementation framework is a backbone program that implements the programming model. It facilitates the development in reducing the effort on the common tasks of context-awareness and help developers focus on the application-specific components. The development environment provides a series of tools to support the development of context-aware applications. These tools simplify the development process and provide the developed applications with robustness and testability.
35541255
The Finite Element Analysis Based on ANSYS Pressure-Sensitive Conductive Rubber Three-Dimensional Tactile Sensor
Aiming at three-dimensional tactile sensing system based on pressure-sensitive conductive rubber, constitutive relation of large deformed incompressible rubber materials and finite element analyzing method are utilized to conduct analog simulation experiment, and finite element model of flexible tactile sensor sensitive unit is established. Nonlinear analyzing technique of ANSYS software is used to analyze sensitive unit structure. The deformation of pressure-sensitive conductive rubber layer is simulated and the feasibility of design scheme is testified through the comparison of simulation data and experiment data.