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Occurrence of high imbalance in real-world domains is a direct result of rarity of interesting events, which results in skewed datasets. Without dataset rebalancing, the learning algorithm will encounter extremely low minority class samples therefore it gets biased towards the majority class in the classification tasks. Hence properly handling the imbalanced dataset is a crucial issue in the pattern...
This paper proposes a transfer learning scheme for traffic pattern analysis where the transferred classifier could be trained with a small number of samples. First we make feature descriptors to represent the traffic trajectories so that they should be adequate to transfer and classify the traffic patterns. Then, we use support vector machine (SVM) to learn the feature descriptors of traffic trajectories...
Uncovering the hidden subtleties and irregularities of the events in the video sequence, is the key issue for automatic video surveillance. Notice the fact that the occurrence of abnormal events is rare while the frequently occurring events become normal in general human perception. So we have proposed the unsupervised learning algorithm, Proximity (Prx) clustering for abnormality detection in the...
This paper presents a possibility that we can teach what emotions a robot should express. For this, we design an artificial emotion decision system learned by feedbacks of users. The proposed system consists of three parts: a personality space with probability model, an emotion decision process, and an emotion learning process. 1) The personality space is designed based on the Five-Factor Model. In...
This paper proposes a hierarchical and probabilistic approach for interframe motion estimation to eliminate unwanted camera motions. This approach adopts three hierarchies, local motion estimation, region motion estimation, and global motion estimation. The local motion is described as a probability distribution for randomly selected local patches. The region motion is defined as maximum likelihood...
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