Combining the Right Features for Complex Event Recognition

In this paper, we tackle the problem of combining fea-tures extracted from video for complex event recognition. Feature combination is an especially relevant task in video data, as there are many features we can extract, rang-ing from image features computed from individual frames to video features that take temporal information into ac-count. To combine features effectively, we propose a method that is able to be selective of different subsets of features, as some features or feature combinations may be unin-formative for certain classes. We introduce a hierarchi-cal method for combining features based on the AND/OR graph structure, where nodes in the graph represent com-binations of different sets of features. Our method auto-matically learns the structure of the AND/OR graph using score-based structure learning, and we introduce an infer-ence procedure that is able to efficiently compute structure scores. We present promising results and analysis on th

ANMM4CBR: a case-based reasoning method for gene expression data classification

<p>Abstract</p> <p>Background</p> <p>Accurate classification of microarray data is critical for successful clinical diagnosis and treatment. The "curse of dimensionality" problem and noise in the data, however, undermines the performance of many algorithms.</p> <p>Method</p> <p>In order to obtain a robust classifier, a novel Additive Nonparametric Margin Maximum for Case-Based Reasoning (ANMM4CBR) method is proposed in this article. ANMM4CBR employs a case-based reasoning (CBR) method for classification. CBR is a suitable paradigm for microarray analysis, where the rules that define the domain knowledge are difficult to obtain because usually only a small number of training samples are available. Moreover, in order to select the most informative genes, we propose to perform feature selection via additively optimizing a nonparametric margin maximum criterion, which is defined based on gene pre-selection and sample clustering. Our feature selection method is very robust to noise in the data.</p> <p>Results</p> <p>The effectiveness of our method is demonstrated on both simulated and real data sets. We show that the ANMM4CBR method performs better than some state-of-the-art methods such as support vector machine (SVM) and <it>k </it>nearest neighbor (<it>k</it>NN), especially when the data contains a high level of noise.</p> <p>Availability</p> <p>The source code is attached as an additional file of this paper.</p

L.: Grouplet: A structured image representation for recognizing human and object interactions

Psychologists have proposed that many human-object interaction activities form unique classes of scenes. Recognizing these scenes is important for many social functions. To enable a computer to do this is however a challenging task. Take people-playing-musical-instrument (PPMI) as an example; to distinguish a person playing violin from a person just holding a violin requires subtle distinction of characteristic image features and feature arrangements that differentiate these two scenes. Most of the existing image representation methods are either too coarse (e.g. BoW) or too sparse (e.g. constellation models) for performing this task. In this paper, we propose a new image feature representation called “grouplet”. The grouplet captures the structured information of an image by encoding a number of discriminative visual features and their spatial configurations. Using a dataset of 7 different PPMI activities, we show that grouplets are more effective in classifying and detecting human-object interactions than other state-of-theart methods. In particular, our method can make a robust distinction between humans playing the instruments and humans co-occurring with the instruments without playing. 1