Image Summaries using Database Saliency

It is useful to have a small set of representative images from a database of thousands of images to summarize its content. There are two key aspects of such image summaries: how to generate them and how to present them. We address both issues. We extend the idea of image saliency to databases and introduce the notion of database saliency. We argue that in image databases, there are certain images that are more uncommon or salient than others and therefore are more interesting. To find such images, we compute their distinctness with respect to the rest of the database. We demonstrate the use of database saliency in two visualization applications: creating image collages and mosaics using automatically chosen salient images

Appearance-based Keypoint Clustering

We present an algorithm for clustering sets of detected interest points into groups that correspond to visually distinct structure. Through the use of a suitable colour and texture representation, our clustering method is able to identify keypoints that belong to separate objects or background regions. These clusters are then used to constrain the matching of keypoints over pairs of images, resulting in greatly improved matching under difficult conditions. We present a thorough evaluation of each component of the algorithm,and show its usefulness on difficult matching problems

SLIC Superpixels Compared to State-of-the-art Superpixel Methods

Computer vision applications have come to rely increasingly on superpixels in recent years, but it is not always clear what constitutes a good superpixel algorithm. In an effort to understand the benefits and drawbacks of existing methods, we empirically compare five state-of-the-art superpixel algorithms for their ability to adhere to image boundaries, speed, memory efficiency, and their impact on segmentation performance. We then introduce a new superpixel algorithm, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels. Despite its simplicity, SLIC adheres to boundaries as well as or better than previous methods. At the same time, it is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation

SLIC Superpixels

Superpixels are becoming increasingly popular for use in computer vision applications. However, there are few algorithms that output a desired number of regular, compact superpixels with a low computational overhead. We introduce a novel algorithm that clusters pixels in the combined five-dimensional color and image plane space to efficiently generate compact, nearly uniform superpixels. The simplicity of our approach makes it extremely easy to use -- a lone parameter specifies the number of superpixels -- and the efficiency of the algorithm makes it very practical. Experiments show that our approach produces superpixels at a lower computational cost while achieving a segmentation quality equal to or greater than four state-of-the-art methods, as measured by boundary recall and under-segmentation error. We also demonstrate the benefits of our superpixel approach in contrast to existing methods for two tasks in which superpixels have already been shown to increase performance over pixel-based methods

Spatio-Chromatic Decorrelation by Shift-Invariant Filtering

In this paper we derive convolutional filters for colour image whitening and decorrelation. Whilst whitening can be achieved via eigendecomposition of the image patch covariance, this operation is neither efficient nor biologically plausible. Given the shift invariance of image statistics, the covariance matrix contains repeated information which can be eliminated by solving directly for a per pixel linear operation (convolution). We formulate decorrelation as a shift and rotation invariant filtering operation and solve directly for the filter shape via non-linear least squares. This results in opponent-colour lateral inhibition filters which resemble those found in the human visual system. We also note the similarity of these filters to current interest point detectors, and perform an experimental evaluation of their use in this context. 1

Scale-Adaptive Superpixels

Size uniformity is one of the prominent features of superpixels. However, size uniformity rarely conforms to the varying content of an image. The chosen size of the superpixels therefore represents a compromise - how to obtain the fewest superpixels without losing too much important detail. We present an image segmentation technique that generates compact clusters of pixels grown sequentially, which automatically adapt to the local texture and scale of an image. Our algorithm liberates the user from the need to choose of the right superpixel size or number. The algorithm is simple and requires just one input parameter. In addition, it is computationally very efficient, approaching real-time performance, and is easily extensible to three-dimensional image stacks and video volumes. We demonstrate that our superpixels superior to the respective state-of-the-art algorithms on quantitative benchmarks

ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE POUR L’OBTENTION DU GRADE DE DOCTEUR ÈS SCIENCES TECHNIQUES PAR

de nationalité allemand

Vegetation and fire history of coastal north-eastern Sardinia (Italy) under changing Holocene climates and land use

Little is known about the vegetation and fire history of Sardinia, and especially the long-term history of the thermo-Mediterranean belt that encompasses its entire coastal lowlands. A new sedimentary record from a coastal lake based on pollen, spores, macrofossils and microscopic charcoal analysis is used to reconstruct the vegetation and fire history in north-eastern Sardinia. During the mid-Holocene (c. 8,100–5,300 cal bp), the vegetation around Stagno di Sa Curcurica was characterised by dense Erica scoparia and E. arborea stands, which were favoured by high fire activity. Fire incidence declined and evergreen broadleaved forests of Quercus ilex expanded at the beginning of the late Holocene. We relate the observed vegetation and fire dynamics to climatic change, specifically moister and cooler summers and drier and milder winters after 5,300 cal bp. Agricultural activities occurred since the Neolithic and intensified after c. 7,000 cal bp. Around 2,750 cal bp, a further decline of fire incidence and Erica communities occurred, while Quercus ilex expanded and open-land communities became more abundant. This vegetation shift coincided with the historically documented beginning of Phoenician period, which was followed by Punic and Roman civilizations in Sardinia. The vegetational change at around 2,750 cal bp was possibly advantaged by a further shift to moister and cooler summers and drier and milder winters. Triggers for climate changes at 5,300 and 2,750 cal bp may have been gradual, orbitally-induced changes in summer and winter insolation, as well as centennial-scale atmospheric reorganizations. Open evergreen broadleaved forests persisted until the twentieth century, when they were partly substituted by widespread artificial pine plantations. Our results imply that highly flammable Erica vegetation, as reconstructed for the mid-Holocene, could re-emerge as a dominant vegetation type due to increasing drought and fire, as anticipated under global change conditions