We present a comprehensive evaluation of performance of visual feature representations for MediaEval 2011- Violent Scenes Detection Task. As for global features, color mo-ments, color histogram, edge orientation histogram, and lo-cal binary patterns are used. As for local features, keypoint

We present a comprehensive evaluation of performance of shot-based visual feature representations for MediaEval 2012- Violent Scenes Detection Affect Task. In spite of using keyframe-based as last year, we try to apply shot-based features using the global features (color moments, color histogram

In this paper, we address the problem of performance evaluation (PE) of video scene detection algorithms. An objective and comprehensive protocol for performance evaluation and comparison of different scene detection algorithms is proposed. Unlike traditional precision-recall measures, the proposed

A novel scene reconstruction technique is presented, different from previous approaches in its ability to cope with large changes in visibility and its modeling of intrinsic scene color and texture information. The method avoids image correspondence problems by working in a discretized scene space

saliency. The system breaks down the complex problem of scene understanding by rapidly selecting, in a computationally efficient manner, conspicuous locations to be analyzed in detail. Index terms: Visual attention, scene analysis, feature extraction, target detection, visual search. \Pi I. Introduction

about the physical properties of the scene are provided by the changes of intensity in the image. The importance of intensity changes and edges in early visual processg has led to extensive research on their detection, description and .use, both in computer and biological vision systems. This article

This paper presents a method for recognizing scene categories based on approximate global geometric correspondence. This technique works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside each sub-region. The resulting “spatial

We present a 3-D shape-based object recognition system for simultaneous recognition of multiple objects in scenes containing clutter and occlusion. Recognition is based on matching surfaces by matching points using the spin-image representation. The spin-image is a data level shape descriptor

Abstract—This paper evaluates the performance of three scene cut detection algorithms for MPEG-coded video data. A large amount of data taken from actual video sequences is presented to evaluate the scene results. For each method, the percentage of false, as well as failed, scene detection is used

The factorization method described in this series of reports requires an algorithm to track the motion of features in an image stream. Given the small inter-frame displacement made possible by the factorization approach, the best tracking method turns out to be the one proposed by Lucas and Kanade in 1981. The method defines the measure of match between fixed-size feature windows in the past and current frame as the sum of squared intensity differences over the windows. The displacement is then defined as the one that minimizes this sum. For small motions, a linearization of the image intensities leads to a Newton-Raphson style minimization. In this report, after rederiving the method in a physically intuitive way, we answer the crucial question of how to choose the feature windows that are best suited for tracking. Our selection criterion is based directly on the definition of the tracking algorithm, and expresses how well a feature can be tracked. As a result, the criterion is optima...