This paper examines the issue of direct extraction of low level features from compressed images. Specifically, we consider the detection of areas of interest and edges in images compressed using the discrete cosine transform (DCT). For interest areas, we show how a measure based on certain DCT coefficients of a block can provide an indication of underlying activity. For edges, we show using an ...

In this paper, we present the Bag-of-Attributes (BoA) model for video representation aiming at video event retrieval. The BoA model is based on a semantic feature space for representing videos, resulting in high-level video feature vectors. For creating a semantic space, i.e., the attribute space, we can train a classifier using a labeled image dataset, obtaining a classification model that can...

A typical content-based information retrieval (CBIR) system, e.g., an image or video retrieval system, includes three major aspects: feature extraction, high dimensional indexing and system design [1]. Among the three aspects, high dimensional indexing is important for speed performance; system design is critical for appearance performance; and feature extraction is the key to accuracy performa...

Robust low-level image features have proven to be effective representations for a variety of high-level visual recognition tasks, such as object recognition and scene classification. But as the visual recognition tasks become more challenging, the semantic gap between low-level feature representation and the meaning of the scenes increases. In this paper, we propose to use objects as attributes...

Machine learning offers a range of tools for training systems from data, but these methods are only as good as the underlying representation. This paper proposes to acquire representations for machine learning by reading text written to accommodate human learning. We propose a novel form of semantic analysis called reading to learn, where the goal is to obtain a high-level semantic abstract of ...

Affective computing researchers adopt a variety of methods in analysing or synthesizing aspects of human behaviour. The choice of method depends on which behavioural cues are considered salient or straightforward to capture and comprehend, as well as the overall context of the interaction. Thus, each approach focuses on modelling certain information and results to dedicated representations. How...

This paper presents a novel approach for automatic people counting in videos captured through a conventional closed-circuit television (CCTV) using computer vision techniques. The proposed approach consists of detecting and tracking moving objects in video scenes to further counting them when they enters into a virtual counting zone defined in the scene. One of the main problems of using conven...

Teaching a robot to perceive and navigate in an unstructured natural world is a difficult task. Without learning, navigation systems are short-range and extremely limited. With learning, the robot can be taught to classify terrain at longer distances, but these classifiers can be fragile as well, leading to extremely conservative planning. A robust, high-level learning-based perception system f...

A central problem in automatic sound recognition is the mapping between low-level audio features and the meaningful content of an auditory scene. We propose a dynamic network model to perform this mapping. In acoustics, much research is devoted to low-level perceptual abilities such as audio feature extraction and grouping, which are translated into successful signal processing techniques. Howe...

This paper proposes a multi-level feature learning framework for human action recognition using body-worn inertial sensors. The framework consists of three phases, respectively designed to analyze signal-based (low-level), components (mid-level) and semantic (high-level) information. Low-level features, extracted from raw signals, capture the time and frequency domain property while mid-level r...