18,616 research outputs found
Metric learning applied for automatic large scale image classification
In the current Internet world, the numbers of digital images are growing exponentially. As a result, it is very tough to retrieve relevant objects for a given query point. For the past few years, researchers have been contributing different algorithms in the two most common machine learning categories to either cluster or classify images. There are several techniques of supervised classification images depending on the local or global feature representation of images, and on the metric used to calculate the distance (or similarity) between images. Recently many studies have shown the interest to learn a metric rather than use a simple metric a priori (e.g. Euclidean distance). This approach is described in the literature as metric learning. The main objective of this thesis is to use metric learning algorithm in the context of large-scale image classification. In this project, we use a metric learning algorithm which is driven by the nearest neighbors approach and has a competence to improve the generic k Nearest Neighbor (kNN) machine learning algorithm. Even though we get significant improvement on the performance of classification, the computation is very expensive due to the large dimensionality of our input dataset. Thus, we use the dimension reduction technique to reduce dimension and computation time as well. Nevertheless, due to the size of the database, classifying and searching a given query point using metric learning algorithm alone exhaustively is intractable
Exemplar Based Deep Discriminative and Shareable Feature Learning for Scene Image Classification
In order to encode the class correlation and class specific information in
image representation, we propose a new local feature learning approach named
Deep Discriminative and Shareable Feature Learning (DDSFL). DDSFL aims to
hierarchically learn feature transformation filter banks to transform raw pixel
image patches to features. The learned filter banks are expected to: (1) encode
common visual patterns of a flexible number of categories; (2) encode
discriminative information; and (3) hierarchically extract patterns at
different visual levels. Particularly, in each single layer of DDSFL, shareable
filters are jointly learned for classes which share the similar patterns.
Discriminative power of the filters is achieved by enforcing the features from
the same category to be close, while features from different categories to be
far away from each other. Furthermore, we also propose two exemplar selection
methods to iteratively select training data for more efficient and effective
learning. Based on the experimental results, DDSFL can achieve very promising
performance, and it also shows great complementary effect to the
state-of-the-art Caffe features.Comment: Pattern Recognition, Elsevier, 201
Active Object Localization in Visual Situations
We describe a method for performing active localization of objects in
instances of visual situations. A visual situation is an abstract
concept---e.g., "a boxing match", "a birthday party", "walking the dog",
"waiting for a bus"---whose image instantiations are linked more by their
common spatial and semantic structure than by low-level visual similarity. Our
system combines given and learned knowledge of the structure of a particular
situation, and adapts that knowledge to a new situation instance as it actively
searches for objects. More specifically, the system learns a set of probability
distributions describing spatial and other relationships among relevant
objects. The system uses those distributions to iteratively sample object
proposals on a test image, but also continually uses information from those
object proposals to adaptively modify the distributions based on what the
system has detected. We test our approach's ability to efficiently localize
objects, using a situation-specific image dataset created by our group. We
compare the results with several baselines and variations on our method, and
demonstrate the strong benefit of using situation knowledge and active
context-driven localization. Finally, we contrast our method with several other
approaches that use context as well as active search for object localization in
images.Comment: 14 page
Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classification
This paper addresses the task of zero-shot image classification. The key
contribution of the proposed approach is to control the semantic embedding of
images -- one of the main ingredients of zero-shot learning -- by formulating
it as a metric learning problem. The optimized empirical criterion associates
two types of sub-task constraints: metric discriminating capacity and accurate
attribute prediction. This results in a novel expression of zero-shot learning
not requiring the notion of class in the training phase: only pairs of
image/attributes, augmented with a consistency indicator, are given as ground
truth. At test time, the learned model can predict the consistency of a test
image with a given set of attributes , allowing flexible ways to produce
recognition inferences. Despite its simplicity, the proposed approach gives
state-of-the-art results on four challenging datasets used for zero-shot
recognition evaluation.Comment: in ECCV 2016, Oct 2016, amsterdam, Netherlands. 201
A comparative study of image processing thresholding algorithms on residual oxide scale detection in stainless steel production lines
The present work is intended for residual oxide scale detection and classification through the application of image processing
techniques. This is a defect that can remain in the surface of stainless steel coils after an incomplete pickling process in a
production line. From a previous detailed study over reflectance of residual oxide defect, we present a comparative study of
algorithms for image segmentation based on thresholding methods. In particular, two computational models based on multi-linear
regression and neural networks will be proposed. A system based on conventional area camera with a special lighting was
installed and fully integrated in an annealing and pickling line for model testing purposes. Finally, model approaches will be
compared and evaluated their performance..Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech
- …