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Group activity recognition from videos is a very challenging problem that has barely been addressed. We propose an activity recognition method using group context. In order to encode both single-person description and two-person interactions, we learn mappings from highdimensional feature spaces to low-dimensional dictionaries. In particular the proposed two-person descriptor takes into account geometric...
This paper presents a data-driven approach towards the modeling of agent behaviors in a full-fledged, commercial off-the-shelf simulation milieu for tactical military training. The modeling approach employs machine learning to identify behavioral rules and patterns in data. Potential advantages of this approach are that it may improve modeling efficiency and, perhaps more importantly, increase the...
Difficulties can arise from the segmentation of three-dimensional objects formed by multiple non-rigid parts represented in two-dimensional images. Problems involving parts whose spatial arrangement is subject to weak restrictions, and whose appearance and form change across images, can be particularly challenging. Segmentation methods that take into account spatial context information have addressed...
In this paper, we present a new shape-based system for person re-identification. The silhouette shape is represented by a Point Distribution Model (PDM) aligned on the body. We improve a fitting model which iteratively adjusts the shape by maximizing a boosted score of local features: the "Boosted Deformable Model". We modify the training procedure with a ranking structure to find how the...
In this paper, we present a novel signature matching method based on supervised topic models. Shape Context features are extracted from signature shape contours which capture the local variations in signature properties. We then use the concept of topic models to learn the shape context features which correspond to individual authors. The approach consists of three primary steps. First, K-means is...
We propose a foreground segmentation method based on convolutional networks. To predict the label of a pixel in an image, the model takes a hierarchical context as the input, which is obtained by combining multiple context patches on different scales. Short range contexts depict the local details, while long range contexts capture the object-scene relationships in an image. Early means that we combine...
State estimation and control are intimately related processes in robot handling of flexible and articulated objects. While for rigid objects, we can generate a CAD model before-hand and a state estimation boils down to estimation of pose or velocity of the object, in case of flexible and articulated objects, such as a cloth, the representation of the object's state is heavily dependent on the task...
In this paper, we propose a novel approach for object detection via foreground feature selection and part-based shape model. It automatically learns a shape model from cluttered training images without need to explicitly given bounding box on objects. Our approach commences by extracting a set of feature descriptors, and iteratively selects the foreground features using Earth Movers Distances based...
Target phrase selection, a crucial component of the state-of-the-art phrase-based statistical machine translation(PBSMT) model, plays a key role in generating accurate translation hypotheses. Inspired by context-rich word-sense disambiguation techniques, machine translation (MT) researchers have successfully integrated various types of source language context into the PBSMT model to improve target...
We present a new method to learn the model based on object parts extraction and grammar which can be applied to classification and recognition. Our approach is invariant to the scale and rotation of the objects. We use Structural Context feature to detect object parts. It is done comparing SC histograms of the model and image. We extract oriented triplets from centers of detected parts. We define...
There has been a growing interest in exploiting contextual information in addition to local features to detect and localize multiple object categories in an image. Context models can efficiently rule out some unlikely combinations or locations of objects and guide detectors to produce a semantically coherent interpretation of a scene. However, the performance benefit from using context models has...
A novel self-supervised discriminative training method for estimating language models for automatic speech recognition (ASR) is proposed. Unlike traditional discriminative training methods that require transcribed speech, only untranscribed speech and a large text corpus is required. An exponential form is assumed for the language model, as done in maximum entropy estimation, but the model is trained...
In recent years, many principled probabilistic definitions for the determination of visual saliency have been proposed. Moreover, there has been increased focus on the role of context in the determination of visual salience. Prior efforts have shed some light on how context may help in predicting the location of, or presence of features associated with an object in the context of detection or recognition...
The problem of writer identification in a multi-script environment is attempted using a two-dimensional (2D) autoregressive (AR) modeling technique. Each writer is represented by a set of 2D AR model coefficients. A method to estimate AR model coefficients is proposed. This method is applied to an image of text written by a specific writer so that AR coefficients are obtained to characterize the writer...
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