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.

Shape is one of the main low level features in content based image retrieval systems (CBIR). This paper proposes a novel CBIR technique based on shape feature. In this technique feature extraction is based on a rectangle that covers a shape. The proposed signature is a Fourier based technique and it is invariant against translation, scaling and rotation. The retrieval performance between some c...

We propose a two-step Event-Level Feature (ELF) learning framework for automatic detection of semantic events. In the first step an elementary-level feature is generated to represent images and videos. Then in the second step an ELF is constructed on top of the elementary features to model each event as a feature vector. Semantic event detectors can be built based on the ELF. Various ELFs are g...

The neocognitron network is analysed from the point of view of the contribution of the di erent layers to the nal classi cation. A variation to the neocognitron which gives improved performance is suggested. This variant combines the low level feature extraction capabilities of the initial layers with alternative classi ers such as LVQ and Class Based Means Clustering. This is shown to give per...

Content-based image retrieval (CBIR) is a new but widelyadopted method for finding images from vast and unannotated image databases. In CBIR images are indexed on the basis of low-level features, such as color, texture, and shape, that can automatically be derived from the visual content of the images. The operation of a CBIR system can be seen as a series of more or less independent processing...

We propose a novel reasoning engine for context-aware ubiquitous computing middleware in this paper. Our reasoning engine supports both rulebased reasoning and machine learning reasoning. Our main contribution is to utilize feature selection method to filter the low-level contexts which are not useful for certain special high-level context reasoning. As a result, rules and learning models in th...

Previous work on human action analysis mainly focuses on designing hand-crafted local features and combining their context information. In this paper, we propose using supervised feature learning as a way to learn spatio-temporal features. More specifically, a modified hidden conditional random field is applied to learn two high-level features conditioned on a certain action label. Among them, ...

This paper proposes a novel approach for the construction and use of multi-feature spaces in image classification. The proposed technique combines low-level descriptors and defines suitable metrics. It aims at representing and measuring similarity between semantically meaningful objects within the defined multi-feature space. The approach finds the best linear combination of predefined visual d...