41 research outputs found
Self-taught Object Localization with Deep Networks
This paper introduces self-taught object localization, a novel approach that
leverages deep convolutional networks trained for whole-image recognition to
localize objects in images without additional human supervision, i.e., without
using any ground-truth bounding boxes for training. The key idea is to analyze
the change in the recognition scores when artificially masking out different
regions of the image. The masking out of a region that includes the object
typically causes a significant drop in recognition score. This idea is embedded
into an agglomerative clustering technique that generates self-taught
localization hypotheses. Our object localization scheme outperforms existing
proposal methods in both precision and recall for small number of subwindow
proposals (e.g., on ILSVRC-2012 it produces a relative gain of 23.4% over the
state-of-the-art for top-1 hypothesis). Furthermore, our experiments show that
the annotations automatically-generated by our method can be used to train
object detectors yielding recognition results remarkably close to those
obtained by training on manually-annotated bounding boxes.Comment: WACV 201
Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images
Convolutional neural networks (CNNs) show impressive performance for image
classification and detection, extending heavily to the medical image domain.
Nevertheless, medical experts are sceptical in these predictions as the
nonlinear multilayer structure resulting in a classification outcome is not
directly graspable. Recently, approaches have been shown which help the user to
understand the discriminative regions within an image which are decisive for
the CNN to conclude to a certain class. Although these approaches could help to
build trust in the CNNs predictions, they are only slightly shown to work with
medical image data which often poses a challenge as the decision for a class
relies on different lesion areas scattered around the entire image. Using the
DiaretDB1 dataset, we show that on retina images different lesion areas
fundamental for diabetic retinopathy are detected on an image level with high
accuracy, comparable or exceeding supervised methods. On lesion level, we
achieve few false positives with high sensitivity, though, the network is
solely trained on image-level labels which do not include information about
existing lesions. Classifying between diseased and healthy images, we achieve
an AUC of 0.954 on the DiaretDB1.Comment: Accepted in Proc. IEEE International Conference on Image Processing
(ICIP), 201
Weakly Supervised Semantic Segmentation of Satellite Images
International audienceWhen one wants to train a neural network to perform semantic segmentation, creating pixel-level annotations for each of the images in the database is a tedious task. If he works with aerial or satellite images, which are usually very large, it is even worse. With that in mind, we investigate how to use image-level annotations in order to perform semantic segmentation. Image-level annotations are much less expensive to acquire than pixel-level annotations, but we lose a lot of information for the training of the model. From the annotations of the images, the model must find by itself how to classify the different regions of the image. In this work, we use the method proposed by Anh and Kwak [1] to produce pixel-level annotation from image level annotation. We compare the overall quality of our generated dataset with the original dataset. In addition, we propose an adaptation of the AffinityNet that allows us to directly perform a semantic segmentation. Our results show that the generated labels lead to the same performances for the training of several segmentation networks. Also, the quality of semantic segmentation performed directly by the AffinityNet and the Random Walk is close to the one of the best fully-supervised approaches