2945869
Feature Subset Selection for Multi-class SVM Based Image Classification
Multi-class image classification can benefit much from feature subset selection. This paper extends an error bound of binary SVMs to a feature subset selection criterion for the multi-class SVMs. By minimizing this criterion, the scale factors assigned to each feature in a kernel function are optimized to identify the important features. This minimization problem can be efficiently solved by gradient-based search techniques, even if hundreds of features are involved. Also, considering that image classification is often a small sample problem, the regularization issue is investigated for this criterion, showing its robustness in this situation. Experimental study on multiple benchmark image data sets demonstrates the effectiveness of the proposed approach.
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
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.
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...
9818080
Automatic image annotation and retrieval using weighted feature selection
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 weighted feature selection 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 various different models to link visual tokens with keywords based on the clustering results of K-means algorithm with weighted feature selection and without feature selection, and evaluated performance using precision, recall and correspondence accuracy using benchmark dataset. The results show that weighted feature selection is better than traditional ones for automatic image annotation and retrieval.
15780361
The Real-Time Networked Data Gathering Systems Based on EtherCAT
Distributed control systems have been growing explosively and the Fieldbus technology has been widely employed for networked data gathering system. However, few Field-bus is real time down to the I(Input)/O(Output) level and most of them have a large number of sub-systems. In this paper we introduce a novel high-speed industry Ethernet bus known as EtherCAT into data gathering system. A networked data gathering system based on EtherCAT is achieved and the experimental results indicate the system can perform well in real-time data transmission.
35136972
Exact Best-Case End-to-End Response Time Analysis for Hard Real-Time Distributed Systems
The prediction of best-case response time is quite necessary to guarantee control accuracy and stability of distributed hard real-time systems. Published time analysis techniques can precisely dealt with the precedence relations between tasks on different processors, but usually ignored or inaccurately estimated the effects from the precedence relations between tasks on the same processor. This leads to a pessimistic analysis. This paper presents an algorithm to analyze the best-case end-to-end response time for hard real-time distributed systems. This algorithm extends the traditional analysis technique by exploiting the precedence relations between tasks on both different processors and the same processor and achieves a more accurate analysis. Simulation results have shown that this algorithm can achieve more accurate results than existing methods.
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.
12607701
An AIS-Based Service Match Model
Aiming at the problem that self-learning ability of the current system usually appears weak during service discovery, a novel artificial immune system (AIS) based service match model is proposed, which is based on the reference to the principles of cellpsilas mutation, evaluation and the quadratic response abilities, as well as simulation on the antibody-antigen identification mechanism. Theoretical analysis and simulations both show that the model can not only increasing the recall ratio and matching speed, but also realizing the function that deriving similar service request from known service request and then searching the best matching service. Furthermore, this service matching model is able to improve systempsilas adaptive learning, memorizing and dynamic evaluating abilities in service matching process.