Learning zeroth class dictionary for human action recognition
In this paper, a discriminative two-phase dictionary learning framework is proposed for classifying human action by sparse shape representations, in which the first-phase dictionary is learned on the selected discriminative frames and the second-phase dictionary is built for recognition using reconstruction errors of the first-phase dictionary as input features. We propose a "zeroth class" trick for detecting undiscriminating frames of the test video and eliminating them before voting on the action categories. Experimental results on benchmarks demonstrate the effectiveness of our method.
Code (0)
등록된 구현이 없습니다.
Tasks
Action RecognitionDictionary LearningTemporal Action LocalizationSimilar Papers 제목 키워드 기반
Group sparsity and geometry constrained dictionary learning for action recognition from depth maps.
Human action recognition based on the depth information provided by commodity depth sensors is an important yet challenging task. The noisy depth maps, different lengths of action sequences, and free styles in performing…
Action RecognitionDictionary LearningMultimodal Activity RecognitionTemporal Action LocalizationSparse Dictionary-based Attributes for Action Recognition and Summarization
We present an approach for dictionary learning of action attributes via information maximization. We unify the class distribution and appearance information into an objective function for learning a sparse dictionary of …
Action RecognitionDictionary LearningTemporal Action LocalizationExtracting Person Names from User Generated Text: Named-Entity Recognition for Combating Human Trafficking
Online escort advertisement websites are widely used for advertising victims of human trafficking. Domain experts agree that advertising multiple people in the same ad is a strong indicator of trafficking. Thus, extracti…
Language ModelingLanguage Modellingnamed-entity-recognitionNamed Entity Recognition+2Cross-label Suppression: A Discriminative and Fast Dictionary Learning with Group Regularization
This paper addresses image classification through learning a compact and discriminative dictionary efficiently. Given a structured dictionary with each atom (columns in the dictionary matrix) related to some label, we pr…
ClassificationComputational EfficiencyDictionary LearningFace Recognition+5Moving poselets: A discriminative and interpretable skeletal motion representation for action recognition
Given a video or time series of skeleton data, action recognition systems perform classification using cues such as motion, appearance, and pose. For the past decade, actions have been modeled using low-level feature rep…
Action RecognitionDictionary LearningMultimodal Activity RecognitionTime Series Analysis