14161644
A Fast Algorithm for Creating a Compact and Discriminative Visual Codebook
In patch-based object recognition, using a compact visual codebook can boost computational efficiency and reduce memory cost. Nevertheless, compared with a large-sized codebook, it also risks the loss of discriminative power. Moreover, creating a compact visual codebook can be very time-consuming, especially when the number of initial visual words is large. In this paper, to minimize its loss of discriminative power, we propose an approach to build a compact visual codebook by maxi- mally preserving the separability of the object classes. Furthermore, a fast algorithm is designed to accomplish this task effortlessly, which can hierarchically merge 10,000 visual words down to 2 in ninety seconds. Experimental study shows that the compact visual codebook created in this way can achieve excellent classification performance even after a considerable reduction in size.
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
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.
8968963
Offense based temporal segmentation for event detection in soccer video
Sports video is regarded as a good testing bed for techniques on content based video analysis and processing. Although partially successful systems have been designed for specific sports domains with limited data, most previous works do not adequately address the problem of temporal segmentation for event detection, nor the event representation problem. In this paper, we present an analysis of soccer video for detecting the semantic notion of <i>offense</i>. It is not only useful as a new semantic concept of sports video analysis, but also provides temporal segmentation for video event detection and representation. We propose a system to detect the offensive unit in soccer video automatically. The offensive unit is then used to calculate new semantics like <i>possession</i>, as well as to detect goal events in video. Experimental results on various sources of soccer video have verified that our approach extracts the new semantic notions successfully and facilitates video event detection and representation
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.
16394212
A pharmacogenomic study on the polymorphic gene response to risperidone in schizophrenia patients
Risperidone is a widely used atypical antipsychotic medication, which has currently raised considerable interest in individual differences in patient response to it. Although risperidone is one of the major atypical anti-psychotics, there is a lack of pharmacogenetic studies on it, especially in Asian populations. This paper investigates the relationship between the -1438 A/G polymorphism of 5-HT2A and response to risperidone treatment. In this study, 130 schizophrenia patients were recruited, who were treated with risperidone monotherapy for eight weeks. Clinical response was assessed on the positive and negative syndrome scale (PANSS) on the day of admission and was subsequently assessed after eight weeks following the treatment. The -1438 A/G polymorphism was identified by direct sequencing. The confounding effects of nongenetic factors were estimated and the baseline symptom score was included as a covariate for adjustment. Association tests between genotype and percentage improvement in total PANSS scores, as well as positive symptom scores and negative symptom scores, were performed using analysis of variance (ANOVA). We find no significant differences in response to the treatment in terms of PANSS or subscore improvements among the subgroups according to different genotypes. Further studies on larger groups are planned to confirm the result.
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.