8,511 research outputs found
The iNaturalist Species Classification and Detection Dataset
Existing image classification datasets used in computer vision tend to have a
uniform distribution of images across object categories. In contrast, the
natural world is heavily imbalanced, as some species are more abundant and
easier to photograph than others. To encourage further progress in challenging
real world conditions we present the iNaturalist species classification and
detection dataset, consisting of 859,000 images from over 5,000 different
species of plants and animals. It features visually similar species, captured
in a wide variety of situations, from all over the world. Images were collected
with different camera types, have varying image quality, feature a large class
imbalance, and have been verified by multiple citizen scientists. We discuss
the collection of the dataset and present extensive baseline experiments using
state-of-the-art computer vision classification and detection models. Results
show that current non-ensemble based methods achieve only 67% top one
classification accuracy, illustrating the difficulty of the dataset.
Specifically, we observe poor results for classes with small numbers of
training examples suggesting more attention is needed in low-shot learning.Comment: CVPR 201
Brachiaria species identification using imaging techniques based on fractal descriptors
The use of a rapid and accurate method in diagnosis and classification of
species and/or cultivars of forage has practical relevance, scientific and
trade in various areas of study. Thus, leaf samples of fodder plant species
\textit{Brachiaria} were previously identified, collected and scanned to be
treated by means of artificial vision to make the database and be used in
subsequent classifications. Forage crops used were: \textit{Brachiaria
decumbens} cv. IPEAN; \textit{Brachiaria ruziziensis} Germain \& Evrard;
\textit{Brachiaria Brizantha} (Hochst. ex. A. Rich.) Stapf; \textit{Brachiaria
arrecta} (Hack.) Stent. and \textit{Brachiaria spp}. The images were analyzed
by the fractal descriptors method, where a set of measures are obtained from
the values of the fractal dimension at different scales. Therefore such values
are used as inputs for a state-of-the-art classifier, the Support Vector
Machine, which finally discriminates the images according to the respective
species.Comment: 7 pages, 5 figure
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A perceptual bias for man-made objects in humans
Ambiguous images are widely recognized as a valuable tool for probing human perception. Perceptual biases that arise when people make judgements about ambiguous images reveal their expectations about the environment. While perceptual biases in early visual processing have been well established, their existence in higher-level vision has been explored only for faces, which may be processed differently from other objects. Here we developed a new, highly versatile method of creating ambiguous hybrid images comprising two component objects belonging to distinct categories. We used these hybrids to measure perceptual biases in object classification and found that images of man-made (manufactured) objects dominated those of naturally occurring (non-man-made) ones in hybrids. This dominance generalised to a broad range of object categories, persisted when the horizontal and vertical elements that dominate man-made objects were removed, and increased with the real-world size of the manufactured object. Our findings show for the first time that people have perceptual biases to see man-made objects and suggest that extended exposure to manufactured environments in our urban-living participants has presumably changed the way that they see the world
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