The paradigm of matching images to a very large dataset has been used for numerous vision tasks and is a powerful one. If the image dataset is large enough, one can expect to find good matches of almost any image to the database, allowing label transfer [3, 15], and image editing or enhancement [6, 11]. Users of this approach will want to know how many images are required, and what features to ...

The crowd-sourced Naturewatch GBIF dataset is used to obtain a species classification dataset containing approximately 1.2 million photos of nearly 20 thousand different species of biological organisms observed in their natural habitat. We present a general hierarchical species identification system based on deep convolutional neural networks trained on the NatureWatch dataset. The dataset cont...

Standardised image databases or rather the lack of them are one of the main weaknesses in the field of content based image retrieval (CBIR). Authors often use their own images or do not specify the source of their datasets. Naturally this makes comparison of results somewhat difficult. While a first approach towards a common colour image set has been taken by the MPEG 7 committee their database...

This work develops a method to inspect the quality of pavement conditions based on images captured from moving vehicles. This task is challenging because the appearance of road surfaces varies tremendously, depending on the construction materials (e.g., concrete, asphalt), the weather conditions (e.g., rain, snow), the illumination conditions (e.g., sunny, shadow), and the interference of other...

We introduce a new light-field dataset of materials, and take advantage of the recent success of deep learning to perform material recognition on the 4D light-field. Our dataset contains 12 material categories, each with 100 images taken with a Lytro Illum, from which we extract about 30,000 patches in total. To the best of our knowledge, this is the first mid-size dataset for light-field image...

In this paper, we present an image and model dataset of the real-life objects from the Yale-CMU-Berkeley Object Set, which is specifically designed for benchmarking in manipulation research. For each object, the dataset presents 600 highresolution RGB images, 600 RGB-D images and five sets of textured three-dimensional geometric models. Segmentation masks and calibration information for each im...

In this paper we propose a benchmark dataset for crop / weed discrimination, single plant phenotyping and other open computer vision tasks in precision agriculture. The dataset comprises 60 images with annotations and is available online. All images were acquired with the autonomous field robot Bonirob in an organic carrot farm while the carrot plants were in early true leaf growth stage. Intra...

This paper describes the Trento Universal Human Object Interaction dataset, TUHOI, which is dedicated to human object interactions in images.1 Recognizing human actions is an important yet challenging task. Most available datasets in this field are limited in numbers of actions and objects. A large dataset with various actions and human object interactions is needed for training and evaluating ...

Effectively measuring the similarity among images is a challenging problem in image retrieval tasks due to the difficulty of considering the dataset manifold. This paper presents an unsupervised manifold learning algorithm that takes into account the intrinsic dataset geometry for defining a more effective distance among images. The dataset structure is modeled in terms of a Correlation Graph (...

We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object...