5317364
A criterion for optimizing kernel parameters in KBDA for image retrieval
A criterion is proposed to optimize the kernel parameters in Kernel-based Biased Discriminant Analysis (KBDA) for image retrieval. Kernel parameter optimization is performed by optimizing the kernel space such that the positive images are well clustered while the negative ones are pushed far away from the positives. The proposed criterion measures the goodness of a kernel space, and the optimal kernel parameter set is obtained by maximizing this criterion. Retrieval experiments on two benchmark image databases demonstrate the effectiveness of proposed criterion for KBDA to achieve the best possible performance at the cost of a small fractional computational overhead.
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.
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.
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
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.
15041864
Research on Neighboring APs Discovery Methods in PnP WLAN
To overcome the disadvantage in traditional manual configuration method for WLANs, such as the high configuration and maintenance cost and low efficiency of system performance, we propose the plug-and-play (PnP) of multi-mode APs that APs auto-configure themselves based on the network environment. In order to implement the PnP, the Multi-mode AP must obtain its neighboring APs' configuration and environment information. Existing neighboring APs discovery method can not provide the precise neighboring APs information to satisfy the requirement of PnP of AP. In this paper, we propose three kinds of neighboring AP discovery and information exchange methods, which are passive discovery method, active discovery method and station assistant discovery method. Using these three neighboring AP discovery methods, AP can discover all neighboring APs and obtain needed information. We further propose two whole process flows, which combine three discovery methods in different manner, to achieve different goal. One process flow is to discover the neighboring AP as fast as possible, called fast discovery process flow. Another one is to discover the neighboring AP with minimal interference to neighboring APs, called the minimal interference process flow. The efficiency of the two process flows is shown in the simulation results.
32146489
Cycle Period Analysis and Optimization of Timed Circuits
In this paper, a method is proposed to analyze the minimum average cycle period of the timed circuits. Timed Petri net is used to model timed circuits. Our method is focus on structural analysis of the Petri net model of the timed circuits, which is another way to reduce the state space of the analyzed model. Then an algorithm is proposed to optimize the performance of timed circuit by asynchronous retiming technique. The algorithm balances the asynchronous pipelines to gain the target cycle period while minimize the area at the same time. Experimental results demonstrate the computational feasibility and effectiveness of both approaches.
30078088
Image Segmentation by a Robust Clustering Algorithm Using Gaussian Estimator
This paper presents a novel clustering-based image segmentation method, which incorporates the features of robust statistics. To overcome the sensitivity to noise and outliers in fuzzy clustering, a simple but efficient M-estimator, Gaussian estimator, has been introduced to clustering analysis as weight or membership function. When applied to image analysis, the proposed Robust Gaussian Clustering/Segmentation (RGCS) algorithm exhibits more reasonable pixel classification and noise suppression performance with respect to FCM. Moreover, by using a uniform resolution parameter scheme, this method avoids producing coincident clusters, which occurs often in possibilistic clustering segmentation. Experiments on both the synthetic data and real image demonstrate the validity and power of the proposed algorithm.
34094601
DNA-Based Algorithm for 0-1 Planning Problem
Biochemical reaction theory based DNA computation is of much better performance in solving a class of intractable computational problems such as NP-complete problems, it is important to study the DNA computation. A novel algorithm based on DNA computation is proposed, which solves a special category of 0-1 planning problem by using the surface-based fluorescence labeling technique. The analysis show that our new algorithm is of significant advantages such as simple encoding, low cost and short operating time, etc.
30689090
Modeling and simulation for crosstalk aggravated by weak-bridge defects between on-chip interconnects
This paper presents comprehensive analytic models that consider both the case of a weak bridge, and the combination of a weak bridge and crosstalk between two interconnects. Our models capture the induced signal delay and pulse as a function of the parameters of the circuit and input signals. Our results are compared with HSPICE and shown to be accurate. A simulator is developed that implements our models and accurately captures timing (delay) characteristics of a circuit. We contrast our results with others, and show the benefits of this new model as well as the ability to predict the range of resistance that leads to delay errors.