Unsupervised learning of probabilistic object models (POMs) for object classification, segmentation, and recognition using knowledge propagation

An Energy-Based SAR Image Segmentation Method with Weighted Feature

To extract more structural features, which can contribute to segment a synthetic aperture radar (SAR) image accurately, and explore their roles in the segmentation procedure, this paper presents an energy-based SAR image segmentation method with weighted features. To precisely segment a SAR image, multiple structural features are incorporated into a block- and energy-based segmentation model in weighted way. In this paper, the multiple features of a pixel, involving spectral feature obtained from original SAR image, texture and boundary features extracted by a curvelet transform, form a feature vector. All the pixels’ feature vectors form a feature set of a SAR image. To automatically determine the roles of the multiple features in the segmentation procedure, weight variables are assigned to them. All the weight variables form a weight set. Then the image domain is partitioned into a set of blocks by regular tessellation. Afterwards, an energy function and a non-constrained Gibbs probability distribution are used to combine the feature and weight sets to build a block-based energy segmentation model with feature weighted on the partitioned image domain. Further, a reversible jump Markov Chain Monte Carlo (RJMCMC) algorithm is designed to simulate from the segmentation model. In the RJMCMC algorithm, three move types were designed according to the segmentation model. Finally, the proposed method was tested on the SAR images, and the quantitative and qualitative results demonstrated its effectiveness.

An online one class support vector machine-based person-specific fall detection system for monitoring an elderly individual in a room environment

In this paper, we propose a novel computer vision-based fall detection system for monitoring an elderly person in a home care, assistive living application. Initially, a single camera covering the full view of the room environment is used for the video recording of an elderly person's daily activities for a certain time period. The recorded video is then manually segmented into short video clips containing normal postures, which are used to compose the normal dataset. We use the codebook background subtraction technique to extract the human body silhouettes from the video clips in the normal dataset and information from ellipse fitting and shape description, together with position information, is used to provide features to describe the extracted posture silhouettes. The features are collected and an online one class support vector machine (OCSVM) method is applied to find the region in feature space to distinguish normal daily postures and abnormal postures such as falls. The resultant OCSVM model can also be updated by using the online scheme to adapt to new emerging normal postures and certain rules are added to reduce false alarm rate and thereby improve fall detection performance. From the comprehensive experimental evaluations on datasets for 12 people, we confirm that our proposed person-specific fall detection system can achieve excellent fall detection performance with 100% fall detection rate and only 3% false detection rate with the optimally tuned parameters. This work is a semiunsupervised fall detection system from a system perspective because although an unsupervised-type algorithm (OCSVM) is applied, human intervention is needed for segmenting and selecting of video clips containing normal postures. As such, our research represents a step toward a complete unsupervised fall detection system.

Object recognition by discriminative combinations of line segments, ellipses, and appearance features

We present a novel contour-based approach that recognizes object classes in real-world scenes using simple and generic shape primitives of line segments and ellipses. Compared to commonly used contour fragment features, these primitives support more efficient representation since their storage requirements are independent of object size. Additionally, these primitives are readily described by their geometrical properties and hence afford very efficient feature comparison. We pair these primitives as shape-tokens and learn discriminative combinations of shape-tokens. Here, we allow each combination to have a variable number of shape-tokens. This, coupled with the generic nature of primitives, enables a variety of class-specific shape structures to be learned. Building on the contour-based method, we propose a new hybrid recognition method that combines shape and appearance features. Each discriminative combination can vary in the number and the types of features, where these two degrees of variability empower the hybrid method with even more flexibility and discriminative potential. We evaluate our methods across a large number of challenging classes, and obtain very competitive results against other methods. These results show the proposed shape primitives are indeed sufficiently powerful to recognize object classes in complex real-world scenes.

NEW EDGE CHARACTERISTICS FOR SCENE AND OBJECT CLASSIFICATION

In this paper, we show that simple edge characteristics in images, when judiciously combined, can result in improved scene and object classification. Unlike existing methods that require a large number of training samples and complex learning schemes, our method discovers simple edge properties. We introduce three sets of edge properties, namely, centroid, compactness and aspect ratio of edges in the image. The combinations of these edge properties are used to discriminate among images in each class. A class representative is calculated for each class according to the average percentage of edges that satisfy the property of a particular class. This percentage for an unknown image is compared to the class representative to assign a label to it. It is shown that this simple edge properties-based method outperforms some of the state-of-the-art results on scene and object classification on standard databases.