Compressed domain action classification using HMM
نویسندگان
چکیده
منابع مشابه
Compressed domain action classification using HMM
This paper proposes three techniques of feature extraction for person independent action classification in compressed MPEG video. The features used are extracted from motion vectors, obtained by partial decoding of the MPEG video. The feature vectors are fed to Hidden Markov Model (HMM) for classification of actions. Totally seven actions were trained with distinct HMM for classification. Recog...
متن کاملAction classification using a discriminative multilevel HDP-HMM
We classify human actions occurring in depth image sequences using features based on skeletal joint positions. The action classes are represented by a multi-level Hierarchical Dirichlet Process-Hidden Markov Model (HDP-HMM). The non-parametric HDP-HMM allows the inference of hidden states automatically from training data. The model parameters of each class are formulated as transformations from...
متن کاملEfficient Domain Action Classification Using Neural Networks
Speaker’s intentions can be represented into domain actions (domainindependent speech acts and domain-dependent concept sequences). Therefore, domain action classification is very useful to a dialogue system that should catch user’s intention in order to generate correct reaction. In this paper, we propose a neural network model to determine speech acts and concept sequences at the same time. T...
متن کاملScene classification in compressed and constrained domain
Holistic representations of natural scenes are an effective and powerful source of information for semantic classification and analysis of images. Despite the technological hardware and software advances, consumer single-sensor imaging devices technology are quite far from the ability of recognising scenes and/or to exploit the visual content during (or after) acquisition time. The frequency do...
متن کاملImage Classification in the Compressed Domain
For content-based image classification and retrieval applications, the adoption of compressed domain features is due more to the improvement of processing efficiencies than the enhancement of content characterization accuracies. To address the second issue in addition to the first, we propose to modify the compressed domain features such that their content characterization capabilities can be i...
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: Pattern Recognition Letters
سال: 2002
ISSN: 0167-8655
DOI: 10.1016/s0167-8655(02)00067-3