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.
120181133
Face recognition based on discriminative manifold learning
In this paper, a discriminative manifold learning method for face recognition is proposed which achieved the discriminative embedding the high dimensional face data into a low dimensional hidden manifold. Unlike the recently proposed LLE, Isomap and Eigenmap algorithms, which are based on reconstruction purpose, our method uses the RCA algorithm to achieve nonlinear embedding and data discrimination at the same time. Also, the LLE and Isomap algorithms are crucially depends on the appropriateness of the neighborhood construction rule, in this paper, a CK-nearest neighborhood rule is proposed to achieve better neighborhood construction. Experimental results indicate the promising performance of the proposed method.
17470669
Retrieval with knowledge-driven kernel design: an approach to improving SVM-based CBIR with relevance feedback
The performance of SVM-based image retrieval is often constrained by the scarcity of training samples. The total number of image samples labeled by users in a retrieval session is very limited, and this small number of labeled samples cannot effectively represent the true distributions of positive and negative image classes, especially for the negative image class. This paper proposes a novel approach to deal with this problem. Instead of treating it as a problem, the mere existence of the small number of labeled images and their desired distribution in the kernel space is considered as prior knowledge from image retrieval to aid the design of the kernel used by SVMs. This is achieved by maximizing a criterion, such as one based on scatter matrices, through gradient-based search methods, incurring very little computational overhead to real-time retrieval process. Experimental results on two benchmark image databases demonstrate the improved retrieval performance by the dynamically designed kernel and hence the effectiveness of the proposed approach for SVM based image retrieval
12805970
A Kernel-Induced Space Selection Approach to Model Selection in KLDA
<para> Model selection in kernel linear discriminant analysis (KLDA) refers to the selection of appropriate parameters of a kernel function and the regularizer. By following the principle of <emphasis emphasistype="bold"><emphasis emphasistype="italic">maximum information preservation</emphasis></emphasis>, this paper formulates the model selection problem as a problem of selecting an optimal kernel-induced space in which different classes are maximally separated from each other. A scatter-matrix-based criterion is developed to measure the “goodness” of a kernel-induced space, and the kernel parameters are tuned by maximizing this criterion. This criterion is computationally efficient and is differentiable with respect to the kernel parameters. Compared with the leave-one-out (LOO) or <formula formulatype="inline"><tex Notation="TeX">$k$</tex></formula>-fold cross validation (CV), the proposed approach can achieve a faster model selection, especially when the number of training samples is large or when many kernel parameters need to be tuned. To tune the regularization parameter in the KLDA, our criterion is used together with the method proposed by Saadi <etal/> (2004). Experiments on benchmark data sets verify the effectiveness of this model selection approach. </para>
5808505
Bootstrapping SVM active learning by incorporating unlabelled images for image retrieval
The performance of image retrieval with SVM active learning is known to be poor when started with few labeled images only. In this paper, the problem is solved by incorporating the unlabelled images into the bootstrapping of the learning process. In this work, the initial SVM classifier is trained with the few labeled images and the unlabelled images randomly selected from the image database. Both theoretical analysis and experimental results show that by incorporating unlabelled images in the bootstrapping, the efficiency of SVM active learning can be improved, and thus improves the overall retrieval performance.
6464793
Ringed Petersen spheres connected hypercube interconnection networks
On the basis of the short diameter of Petersen graph, and high connectivity of hypercube, a novel interconnection networks, RPSCH(n) (ringed Petersen spheres connected hypercube) is proposed, and whose characteristics are studied simultaneously. It is proved that RPSCH(n) is not only of regularity and good extensibility, but also of shorter diameter and better connectivity than those previous well-known interconnection networks such as Q/sub n/, RP(n) and HP(n). In addition, the unicasting and broadcasting routing algorithms are designed for RPSCH(n), whose communication efficiency are proved to be n-2 and n-2 respectively.
171642
Function Call Flow based Fitness Function Design in Evolutionary Testing
Evolutionary Testing has been shown a promising technology for the automatic test data generation. It reformulates test data generation as a metaheuristic search. A well- designed fitness function is essential to the efficiency of evolutionary search. Many efforts have been directed at the design and implementation of fitness function in recent years. However, previous work has just focused on the control dependency of the target on the branches in the same function. When function calls exist in the desired execution trace to the target, the evaluation of the test data on the coverage of these function calls, which should be provided to the evolutionary search, is not captured by the existing fitness function. In this case, the existing fitness function can not fairly evaluate the test data. And the evolutionary search will be hampered or even fail in severe cases. In this paper, a new term is first proposed to incorporate into the existing fitness function. It is applied to evaluate the test data's coverage of function calls along the desired path to the target. The new fitness function can evaluate the test data more fairly, resulting in a better guidance to the evolutionary search. This can be seen by the experiments carried out on two cases.
14024123
Visual Secret Sharing Schemes for Multiple Secret Images Allowing the Rotation of Shares
In this paper, a method is proposed to construct a visual secret sharing (VSS) scheme for multiple secret images in which each share can be rotated with 180 degrees in decryption. The proposed VSS scheme can encrypt more number of secret images compared with the normal VSS schemes. Furthermore, the proposed technique can be applied to the VSS scheme that allows to turn over some shares in decryption. From the theoretical point of view, it is interesting to note that such VSS schemes cannot be obtained from so-called basis matrices straightforwardly.
27554160
Supporting Partial Component Matching
In this paper we define a formal framework for describing components and gaps or holes (where components can be plugged in) This is based on the theory of interface automata The main focus is to define a component partially satisfying the requirements of a hole A partial plug-in of a hole will result in other holes The definition of a partial plug-in does not result in a unique set of holes, i.e., the resulting holes can have different properties We define an software engineering process which uses the formal framework to complete the component selection and insertion process The process is defined in terms of the possible interactions between a component vendor and a customer seeking a component.
17707568
Object Boundary Detection For Ontology-Based Image Classification
Technology in the field of digital media generates huge amounts of non-textual information, audio, video, and images, along with more familiar textual information. The potential for exchange and retrieval of information is vast and daunting. The key problem in achieving efficient and user-friendly retrieval in the domain of image is the development of a search mechanism to guarantee delivery of minimal irrelevant information (high precision) while insuring that relevant information is not overlooked (high recall). The traditional solution to the problem of image retrieval employs content-based search techniques based on color, histogram, texture or shape features. The traditional solution works well in performing searches in which the user specifies images containing a sample object, or a sample textural pattern, in which the object or pattern is indexed. One can overcome this restriction by indexing images according to meanings rather than objects that appear in images, although this will entail a way of converting objects to meanings. We have solved this problem of creating a meaning based index structure through the design and implementation of a concept-based model using domain dependent ontologies. An ontology is a collection of concepts and their interrelationships which provide an abstract view of an application domain. With regard to converting objects to meaning the key issue is to identify appropriate concepts that both describe and identify images. For this, first we need to identify all object boundaries accurately that appear in images. We propose an automatic scalable object boundary detection algorithm based on edge detection and region growing techniques. We also propose an efficient merging algorithm to join adjacent regions using an adjacency graph to avoid the over-segmentation of regions. To illustrate the effectiveness of our algorithm in automatic image classification we implement a very basic system aimed at the classification of images in the sports domain. By identifying objects in images, we show that our approach works well when objects in images have less complex organization.