Search and retrieval of multimedia content based on the evoked emotion comprises an interesting scientific field with numerous applications. This paper proposes a method that fuses two heterogeneous modalities, i.e. m...
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ISBN:
(纸本)9781728127118
Search and retrieval of multimedia content based on the evoked emotion comprises an interesting scientific field with numerous applications. This paper proposes a method that fuses two heterogeneous modalities, i.e. music and electroencephalographic signals, both for predicting emotional dimensions in the valence-arousal plane and for addressing four binary classification tasks, namely i.e. high/low arousal, positive/negative valence, high/low dominance, high/low liking. The proposed solution exploits Mel-scaled and EEG spectrograms feeding a k-medoidsclustering scheme based on canonical correlation analysis. A thorough experimental campaign carried out on a publicly available dataset confirms the efficacy of such an approach. Despite its low computational cost, it was able to surpass state of the art results, and most importantly, in a user-independent manner.
This paper presents a pork quality evaluation method based on the hyperspectral image datasets of 96 pork samples in the range of 400-1000 nm. First, through the k-medoids clustering algorithm based on manifold distan...
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This paper presents a pork quality evaluation method based on the hyperspectral image datasets of 96 pork samples in the range of 400-1000 nm. First, through the k-medoids clustering algorithm based on manifold distance, 30 important wavelengths are selected from 753 wavelengths, and final 8 optimum wavelengths are obtained based on the discriminant value and the spectral resolution. Then, the two-dimensional Gabor wavelet transform is used to extract the eight texture features of the image under the final eight wavelengths respectively, to form a 64-dimensional features of pork quality. Finally, using the fussy C-means (FCM) algorithm based on Isomap dimension reduction, the pork quality evaluation model is constructed. The result of wavelength extraction experiments show that although there is a strong linear correlation between adjacent bands in the hyperspectral image, there is an obvious nonlinear manifold relation in the whole band. Therefore, the k-medoids clustering algorithm based on manifold distance in this paper is more reasonable than the traditional principal component analysis (PCA) in characteristic wavelength selection. According to the experiment of pork quality evaluation, two-dimensional Gabor wavelet transform can extract the texture characteristics of pork better. Compared with the FCM algorithm based on PCA, the FCM algorithm based on Isomap can better solve the high-dimensional clustering problem, and can distinguish fresh chilled meat, frozen-thawed meat and spoiled meat accurately. The study shows that hyperspectral image technology can be used in pork quality evaluation.
A k-medoids clustering algorithm for mixed feature-type symbolic data represented by categorical, interval-valued and histogram-valued is presented in this paper. The algorithm furnishes a partition and a prototype to...
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ISBN:
(纸本)9781457706530
A k-medoids clustering algorithm for mixed feature-type symbolic data represented by categorical, interval-valued and histogram-valued is presented in this paper. The algorithm furnishes a partition and a prototype to each class by optimizing an adequacy criterion based on a suitable standardized Euclidean distance. To evaluate the proposed algorithm, several real symbolic data sets are considered and the results furnished by this algorithm are compared with the results furnished by a partitional algorithm for mixed feature-type symbolic data of the literature of symbolic data analysis in terms of the the correct Rand index.
In this study, a vehicle routing problem with hard time windows (VRPHTW) that appears to meet demands of customers' service within time intervals in a supermarket chain is solved. In VRPHTW, to reach a solution by...
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In this study, a vehicle routing problem with hard time windows (VRPHTW) that appears to meet demands of customers' service within time intervals in a supermarket chain is solved. In VRPHTW, to reach a solution by an exact method is quite difficult and sometimes impossible if number of constraints is large enough (i.e., NP-hard), and solution time of a VRPHTW grows exponentially. As the size of the problem grows, a near optimal solution can be found using a heuristic method. A hierarchical approach consisting of two stages as "cluster-first route-second" is proposed. In the first stage, customers are assigned to vehicles using three different clusteringalgorithms (i.e., k-means, k-medoids and DBSCAN). In the second stage, a VRPHTW is solved using a MILP. The main contribution of the article is that the proposed hierarchical approach enables us to deal with a large size real problem and to solve it in a short time using the exact method. Finally, the proposed approach is employed on a supermarket chain. An instance of the algorithm is demonstrated to illustrate the applicability of the proposed approach and the results obtained are highly favourable.
In this paper, moving object detection under the dynamic background in aerial videos has been studied, and a moving vehicle detection algorithm based on the motion vector is proposed. using the kLT algorithm for match...
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ISBN:
(纸本)9783642273339
In this paper, moving object detection under the dynamic background in aerial videos has been studied, and a moving vehicle detection algorithm based on the motion vector is proposed. using the kLT algorithm for matching feature points, according to k-medoids clustering algorithm on the feature points, finally the location of the target vehicle has been found. Experiments show that the method in this article can accurately detect the moving vehicles in the aerial videos, and then extract the corresponding traffic information.
The classic TSP problem was researched on and CHN144 was chosen to be the data for research. The method that combined MDP and k-medoids was proposed to solve TSP problem in this paper. First of all, cluster CHN144 dat...
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ISBN:
(纸本)9783642319679
The classic TSP problem was researched on and CHN144 was chosen to be the data for research. The method that combined MDP and k-medoids was proposed to solve TSP problem in this paper. First of all, cluster CHN144 data through k-medoids and find out the representative objects respectively. Furthermore, the simple TSP problem that consists of representative objects was solved to acquire the optimal path through the Markov Decision Process. Finally, the global optimal path was acquired as 30445km by using the solution above iteratively to the clustering of each object respectively. The feasibility and superiority of this method was proved by analyzing the experiments we conducted in this paper.
A novel human action recognition algorithm based on key posture is proposed in this *** the method,the mesh features of each image in human action sequences are firstly calculated;then the key postures of the human me...
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A novel human action recognition algorithm based on key posture is proposed in this *** the method,the mesh features of each image in human action sequences are firstly calculated;then the key postures of the human mesh features are generated through k-medoids clustering algorithm;and the motion sequences are thus represented as vectors of key *** component of the vector is the occurrence number of the corresponding posture included in the *** human action recognition,the observed action is firstly changed into key posture vector;then the correlevant coefficients to the training samples are calculated and the action which best matches the observed sequence is chosen as the final *** experiments on Weizmann dataset demonstrate that our method is effective for human action *** average recognition accuracy can exceed 90%.
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