Regionlets for Generic Object Detection.

Generic object detection is confronted by dealing with different degrees of variations, caused by viewpoints or deformations in distinct object classes, with tractable computations. This demands for descriptive and flexible object representations which can be efficiently evaluated in many locations. We propose to model an object class with a cascaded boosting classifier which integrates various types of features from competing local regions, each of which may consist of a group of subregions, named as regionlets. A regionlet is a base feature extraction region defined proportionally to a detection window at an arbitrary resolution (i.e., size and aspect ratio). These regionlets are organized in small groups with stable relative positions to be descriptive to delineate fine-grained spatial layouts inside objects. Their features are aggregated into a one-dimensional feature within one group so as to be flexible to tolerate deformations. The most discriminative regionlets for each object class are selected through a boosting learning procedure. Our regionlet approach achieves very competitive performance on popular multi-class detection benchmark datasets with a single method, without any context. It achieves a detection mean average precision of 41.7 percent on the PASCAL VOC 2007 dataset, and 39.7 percent on the VOC 2010 for 20 object categories. We further develop support pixel integral images to efficiently augment regionlet features with the responses learned by deep convolutional neural networks. Our regionlet based method won second place in the ImageNet Large Scale Visual Object Recognition Challenge (ILSVRC 2013).

iHelp: an intelligent online helpdesk system.

Due to the importance of high-quality customer service, many companies use intelligent helpdesk systems (e.g., case-based systems) to improve customer service quality. However, these systems face two challenges: 1) Case retrieval measures: most case-based systems use traditional keyword-matching-based ranking schemes for case retrieval and have difficulty to capture the semantic meanings of cases and 2) result representation: most case-based systems return a list of past cases ranked by their relevance to a new request, and customers have to go through the list and examine the cases one by one to identify their desired cases. To address these challenges, we develop iHelp, an intelligent online helpdesk system, to automatically find problem-solution patterns from the past customer-representative interactions. When a new customer request arrives, iHelp searches and ranks the past cases based on their semantic relevance to the request, groups the relevant cases into different clusters using a mixture language model and symmetric matrix factorization, and summarizes each case cluster to generate recommended solutions. Case and user studies have been conducted to show the full functionality and the effectiveness of iHelp.

Feature selection for gene expression using model-based entropy.

Gene expression data usually contain a large number of genes but a small number of samples. Feature selection for gene expression data aims at finding a set of genes that best discriminate biological samples of different types. Using machine learning techniques, traditional gene selection based on empirical mutual information suffers the data sparseness issue due to the small number of samples. To overcome the sparseness issue, we propose a model-based approach to estimate the entropy of class variables on the model, instead of on the data themselves. Here, we use multivariate normal distributions to fit the data, because multivariate normal distributions have maximum entropy among all real-valued distributions with a specified mean and standard deviation and are widely used to approximate various distributions. Given that the data follow a multivariate normal distribution, since the conditional distribution of class variables given the selected features is a normal distribution, its entropy can be computed with the log-determinant of its covariance matrix. Because of the large number of genes, the computation of all possible log-determinants is not efficient. We propose several algorithms to largely reduce the computational cost. The experiments on seven gene data sets and the comparison with other five approaches show the accuracy of the multivariate Gaussian generative model for feature selection, and the efficiency of our algorithms.