Automatic image annotation and tagging is necessary for indexing and searching of images using querying a text. It is widely used in search engines like Google, Yahoo, Baidu, etc. Fast image Tagging (FastTag) algorith...
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Automatic image annotation and tagging is necessary for indexing and searching of images using querying a text. It is widely used in search engines like Google, Yahoo, Baidu, etc. Fast image Tagging (FastTag) algorithm is proposed to accelerate image annotation process, while keeping the precision of automatic image annotation results. Feature mapping is used to map image features vectors onto higher dimensional feature space. Feature mapping methods plays an important role in automatic image annotation. In this paper, we have compared 6 kernels, among which four kernels are used in homogeneous feature mapping and two kernels are used in discriminative tree based feature mapping, to investigate which feature mapping performs better for automatic image annotation. The performance of these methods has been analyzed by conducting intensive experiments on three different datasets as used by FastTag algorithm in their experiments. We have found that the homogeneous feature mapping with χ 2 kernel is more suitable when used in FastTag algorithm in terms of precision, recall, FI score and N+ measures, and with a relatively acceptable performance.
Recently, spatial principal component analysis of census transform histograms (PACT) was proposed to recognize instance and categories of places or scenes in an image. An improved representation called Local Differenc...
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Kernel independent component analysis (KICA) has been widely used in the field of blind source separation. The selection of kernel function and its parameters plays an important role in KICA algorithm performance. An ...
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ISBN:
(纸本)9781479979684
Kernel independent component analysis (KICA) has been widely used in the field of blind source separation. The selection of kernel function and its parameters plays an important role in KICA algorithm performance. An optimal kernel model should be rich enough to well map the given samples. However, users usually use a singular kernel based model in their experiments, which leads to a suboptimal kernel model. In order to solve this problem, we propose the evolution based multiple kernel independent component analysis (EMKICA), in which a convex combination of multiple base kernels is used instead of single kernel of KICA. The combination weights are learned by particle swarm optimization algorithm. Firstly, we elaborate the basic theory of KICA and concept of EMKICA, also the combination form of the composition kernel used in EMKICA. Secondly, we describe the presentation of the individuals in the particle swarm optimization algorithm, the settings of the evaluation function and general algorithm. Finally, we evaluate the separation ability of EMKICA on three different data sets including one-dimensional mixed signals, composite images and images with reflection. The experimental results verify the effectiveness of EMKICA.
A new method dealing with recognition of partially occluded and affine distortion objects is presented. The method is designed for objects with smooth curved boundary. It divides an object into affine-invariant parts ...
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A new method dealing with recognition of partially occluded and affine distortion objects is presented. The method is designed for objects with smooth curved boundary. It divides an object into affine-invariant parts based on the feature point. And a new approach for matching each part is presented in this paper. Robust Hausdorff distance (RHD) is introduced to measure the similarity between feature points set of model and that of target. In terms of the new RHD, the optimal affine transform can be estimated. And then the sub-curve match pairs are calculated based on the optimal affine transformation. The experimental results show proposed algorithm are capable of coping with partial occlusion and affine transformation.
In view of the problem that the global optical flow algorithm cannot acquire accurate motion parameter estimation at a low-gradient value, an improved method has been presented in order to enhance the self-adaptive ab...
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DNA sequence homology is a critical and fundamental problem in bioinformatics. In this paper, we solve this problem by use of the second order Markov modal instead of traditional sequence alignment because DNA charact...
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At present, in the field of pixel-level image fusion, researchers tend to treat each pixel independently, which destroys the relationship between the images to be fused. In view of this defects, this paper aims to pro...
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A vertex separator in an undirected graph is a subset of the vertices, whose removal disconnects the graph in at least two nonempty connected components. Given a connected undirected graph G = (V ,E) with |V| = n, an ...
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In this paper, we present a new speech enhancement method based on robust principal component analysis. In the proposed method, noisy signal is transformed into time-frequency domain where background noise is assumed ...
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This paper introduces the theory of ϕ-Jensen variance. Our main motivation is to extend the connotation of the analysis of variance and facilitate its applications in probability, statistics and higher education. To t...
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