106,005 research outputs found
Modelling Local Deep Convolutional Neural Network Features to Improve Fine-Grained Image Classification
We propose a local modelling approach using deep convolutional neural
networks (CNNs) for fine-grained image classification. Recently, deep CNNs
trained from large datasets have considerably improved the performance of
object recognition. However, to date there has been limited work using these
deep CNNs as local feature extractors. This partly stems from CNNs having
internal representations which are high dimensional, thereby making such
representations difficult to model using stochastic models. To overcome this
issue, we propose to reduce the dimensionality of one of the internal fully
connected layers, in conjunction with layer-restricted retraining to avoid
retraining the entire network. The distribution of low-dimensional features
obtained from the modified layer is then modelled using a Gaussian mixture
model. Comparative experiments show that considerable performance improvements
can be achieved on the challenging Fish and UEC FOOD-100 datasets.Comment: 5 pages, three figure
Training a Fast Object Detector for LiDAR Range Images Using Labeled Data from Sensors with Higher Resolution
In this paper, we describe a strategy for training neural networks for object
detection in range images obtained from one type of LiDAR sensor using labeled
data from a different type of LiDAR sensor. Additionally, an efficient model
for object detection in range images for use in self-driving cars is presented.
Currently, the highest performing algorithms for object detection from LiDAR
measurements are based on neural networks. Training these networks using
supervised learning requires large annotated datasets. Therefore, most research
using neural networks for object detection from LiDAR point clouds is conducted
on a very small number of publicly available datasets. Consequently, only a
small number of sensor types are used. We use an existing annotated dataset to
train a neural network that can be used with a LiDAR sensor that has a lower
resolution than the one used for recording the annotated dataset. This is done
by simulating data from the lower resolution LiDAR sensor based on the higher
resolution dataset. Furthermore, improvements to models that use LiDAR range
images for object detection are presented. The results are validated using both
simulated sensor data and data from an actual lower resolution sensor mounted
to a research vehicle. It is shown that the model can detect objects from
360{\deg} range images in real time
Profile approach for recognition of three-dimensional magnetic structures
We propose an approach for low-dimensional visualisation and classification
of complex topological magnetic structures formed in magnetic materials. Within
the approach one converts a three-dimensional magnetic configuration to a
vector containing the only components of the spins that are parallel to the z
axis. The next crucial step is to sort the vector elements in ascending or
descending order. Having visualized profiles of the sorted spin vectors one can
distinguish configurations belonging to different phases even with the same
total magnetization. For instance, spin spiral and paramagnetic states with
zero total magnetic moment can be easily identified. Being combined with a
simplest neural network our profile approach provides a very accurate phase
classification for three-dimensional magnets characterized by complex
multispiral states even in the critical areas close to phases transitions. By
the example of the skyrmionic configurations we show that profile approach can
be used to separate the states belonging to the same phase
Image mining: issues, frameworks and techniques
[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in significantly large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an
interdisciplinary endeavor that draws upon expertise in
computer vision, image processing, image retrieval, data
mining, machine learning, database, and artificial
intelligence. Despite the development of many
applications and algorithms in the individual research
fields cited above, research in image mining is still in its infancy. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining at the end of this paper
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