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Unsupervised learning of invariant representations that efficiently describe high-dimensional time series has several applications in dynamic visual data analysis. Clearly, the problem becomes more challenging when dealing with multiple time series arising from different modalities. A prominent example of this multimodal setting is the human motion which can be represented by multimodal time series...
In this paper, we deal with the classification of Greek folk songs into 8 classes associated with the region of origin of the songs. Motivated by the way the sound is perceived by the human auditory system, auditory cortical representations are extracted from the music recordings. Moreover, deep canonical correlation analysis (DCCA) is applied to the auditory cortical representations for dimensionality...
Dance traditions constitute a significant aspect of cultural heritage around the world. The organization, semantic analysis, and retrieval of dance-related multimedia content (i.e., music, video) in databases is, therefore, crucial to their preservation. In this paper we explore the problem of folk dances recognition from video recordings, focusing on Greek folk dances, using different representations...
In this paper we present two algorithms for efficient person recognition operating upon motion capture data, depicting persons performing various everyday activities. The first approach is driven from the assumption that, if two motion sequences depict a certain activity performed by the same person, then, consecutive frames (poses) of one sequence are expected to be similar to consecutive frames...
In this paper we present an algorithm for efficient activity recognition operating upon human skeleton motion sequences, derived through motion capture systems or by analyzing the output of RGB-D sensors. Our approach is driven from the assumption that, if two such sequences describe similar activities, then, consecutive frames (poses) of one sequence are expected to be similar to consecutive frames...
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