Motion Key-Frame Extraction by Using Optimized t-Stochastic Neighbor Embedding
نویسندگان
چکیده
منابع مشابه
Motion Key-Frame Extraction by Using Optimized t-Stochastic Neighbor Embedding
Key-frame extracting technology has been widely used in the field of human motion synthesis. Efficient and accurate key frames extraction methods can improve the accuracy of motion synthesis. In this paper, we use an optimized t-Stochastic Neighbor Embedding (t-SNE for short) algorithm to reduce the data and on this basis extract the key frames. The experimental results show that the validity o...
متن کاملKey Frame Extraction from Motion Capture Data by Curve Saliency
We propose a new method for extracting key frames from a motion capture sequence. Our proposed approach consists of two steps. In the first step, we propose a new metric, curve saliency, for motion curves that specifies the important frames of the motion. In the second step, we detect the final key frames by clustering the computed important frames. As a result of our experimental results, on t...
متن کاملHierarchical Stochastic Neighbor Embedding
In recent years, dimensionality-reduction techniques have been developed and are widely used for hypothesis generation in Exploratory Data Analysis. However, these techniques are confronted with overcoming the trade-off between computation time and the quality of the provided dimensionality reduction. In this work, we address this limitation, by introducing Hierarchical Stochastic Neighbor Embe...
متن کاملStochastic Neighbor Embedding
We describe a probabilistic approach to the task of placing objects, described by high-dimensional vectors or by pairwise dissimilarities, in a low-dimensional space in a way that preserves neighbor identities. A Gaussian is centered on each object in the high-dimensional space and the densities under this Gaussian (or the given dissimilarities) are used to define a probability distribution ove...
متن کاملIntrinsic t-Stochastic Neighbor Embedding for Visualization and Outlier Detection
Abstract. Analyzing high-dimensional data poses many challenges due to the “curse of dimensionality”. Not all high-dimensional data exhibit these characteristics because many data sets have correlations, which led to the notion of intrinsic dimensionality. Intrinsic dimensionality describes the local behavior of data on a low-dimensional manifold within the higher dimensional space. We discuss ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Symmetry
سال: 2015
ISSN: 2073-8994
DOI: 10.3390/sym7020395