One of the great challenges today is to index videos with high-level semantic concepts or features. The basis of our approach is to use a fuzzy decision trees (FDT) to construct the heart of the system in order to reduce the need of human usage in the process of indexation. But when we address large, unbalanced, multiclass data sets, a single classifier such as the FDT is insufficient. Therefor...

In general the users are in need to retrieve images from a collection of database images from variety of domains. In earlier phase this need was satisfied by retrieving the relevant images from different database simply. Where there is a bottleneck that the images retrieved was not relevant much to the user query because the images were not retrieved based on content where another drawback is t...

We describe a system which is designed to assist animators in extracting high-level information from sequences of images. The system is not meant to replace animators, but to be a tool to assist them in creating the first ‘roughcut’ of a sequence quickly and easily. Using the system, short animations have been created in a very short space of time. We show that the method of principal component...

A novel approach for estimating articulated body posture and motion from monocular video sequences is proposed. Human pose is defined as the instantaneous two dimensional configuration (i.e.,the projection onto the image plane) of a single articulated body in terms of the position of a predetermined set of joints. First, statistical segmentation of the human bodies from the background is perfor...

This paper presents a novel framework for object detection in videos that considers both structural and temporal information. Detection is performed by first applying low-level feature extraction techniques in each frame of the video. Then, additional robustness is obtained by considering the temporal stability of videos, using particle filters and probability maps, which encode information abo...

Mid-level image features have been shown to be helpful to bridge the semantic gap between low-level and high-level image representations. Many existing methods to learn mid-level visual elements consider each mid-level feature individually, and do not take their mutual relationships into account. We follow the intuitive idea that learning discriminative combinations of visual elements can help ...

Recent work in unsupervised feature learning has focused on the goal of discovering high-level features from unlabeled images. Much progress has been made in this direction, but in most cases it is still standard to use a large amount of labeled data in order to construct detectors sensitive to object classes or other complex patterns in the data. In this paper, we aim to test the hypothesis th...

Technology has reached new heights making sound and video capture devices ubiquitous and affordable. We propose a paradigm to exploit this technology for home care applications especially for surveillance and complex event detection. Complex vision tasks such as event detection in a surveillance video can be divided into subtasks such as human detection, tracking, recognition, and trajectory an...

In this paper, we present a new programming model for performing audio analysis, spectral processing, and feature extraction in the ChucK programming language. The solution unifies analysis and synthesis in the same high-level, strongly-timed, and concurrent environment, extending and fully integrating with the existing language framework. In particular, we introduce the notion of a Unit Analyz...

Audio classification typically involves feeding a fixed set of low-level features to a machine learning method, then performing feature aggregation before or after learning. Instead, we jointly learn a selection and hierarchical temporal aggregation of features, achieving significant performance gains. 2010 Elsevier B.V. All rights reserved.