9,237 research outputs found
Toward automated evaluation of interactive segmentation
We previously described a system for evaluating interactive segmentation by means of user experiments (McGuinness and OâConnor, 2010). This method, while effective, is time-consuming and labor-intensive. This paper aims to make evaluation more practicable by investigating if it is feasible to automate user interactions. To this end, we propose a general algorithm for driving the segmentation that uses the ground truth and current segmentation error to automatically simulate user interactions. We investigate four strategies for selecting which pixels will form the next interaction. The first of these is a simple, deterministic strategy; the remaining three strategies are probabilistic, and focus on more realistically approximating a real user. We evaluate four interactive segmentation algorithms using these strategies, and compare the results with our previous user experiment-based evaluation. The results show that automated evaluation is both feasible and useful
Understanding the Limitations of CNN-based Absolute Camera Pose Regression
Visual localization is the task of accurate camera pose estimation in a known
scene. It is a key problem in computer vision and robotics, with applications
including self-driving cars, Structure-from-Motion, SLAM, and Mixed Reality.
Traditionally, the localization problem has been tackled using 3D geometry.
Recently, end-to-end approaches based on convolutional neural networks have
become popular. These methods learn to directly regress the camera pose from an
input image. However, they do not achieve the same level of pose accuracy as 3D
structure-based methods. To understand this behavior, we develop a theoretical
model for camera pose regression. We use our model to predict failure cases for
pose regression techniques and verify our predictions through experiments. We
furthermore use our model to show that pose regression is more closely related
to pose approximation via image retrieval than to accurate pose estimation via
3D structure. A key result is that current approaches do not consistently
outperform a handcrafted image retrieval baseline. This clearly shows that
additional research is needed before pose regression algorithms are ready to
compete with structure-based methods.Comment: Initial version of a paper accepted to CVPR 201
Learning midlevel image features for natural scene and texture classification
This paper deals with coding of natural scenes in order to extract semantic information. We present a new scheme to project natural scenes onto a basis in which each dimension encodes statistically independent information. Basis extraction is performed by independent component analysis (ICA) applied to image patches culled from natural scenes. The study of the resulting coding units (coding filters) extracted from well-chosen categories of images shows that they adapt and respond selectively to discriminant features in natural scenes. Given this basis, we define global and local image signatures relying on the maximal activity of filters on the input image. Locally, the construction of the signature takes into account the spatial distribution of the maximal responses within the image. We propose a criterion to reduce the size of the space of representation for faster computation. The proposed approach is tested in the context of texture classification (111 classes), as well as natural scenes classification (11 categories, 2037 images). Using a common protocol, the other commonly used descriptors have at most 47.7% accuracy on average while our method obtains performances of up to 63.8%. We show that this advantage does not depend on the size of the signature and demonstrate the efficiency of the proposed criterion to select ICA filters and reduce the dimensio
Particular object retrieval with integral max-pooling of CNN activations
Recently, image representation built upon Convolutional Neural Network (CNN)
has been shown to provide effective descriptors for image search, outperforming
pre-CNN features as short-vector representations. Yet such models are not
compatible with geometry-aware re-ranking methods and still outperformed, on
some particular object retrieval benchmarks, by traditional image search
systems relying on precise descriptor matching, geometric re-ranking, or query
expansion. This work revisits both retrieval stages, namely initial search and
re-ranking, by employing the same primitive information derived from the CNN.
We build compact feature vectors that encode several image regions without the
need to feed multiple inputs to the network. Furthermore, we extend integral
images to handle max-pooling on convolutional layer activations, allowing us to
efficiently localize matching objects. The resulting bounding box is finally
used for image re-ranking. As a result, this paper significantly improves
existing CNN-based recognition pipeline: We report for the first time results
competing with traditional methods on the challenging Oxford5k and Paris6k
datasets
Organising a daily visual diary using multifeature clustering
The SenseCam is a prototype device from Microsoft that facilitates automatic capture of images of a person's life by integrating a colour camera, storage media and multiple sensors into a small wearable device. However, efficient search methods are required to reduce the user's burden of sifting through the thousands of images that are captured per day. In this paper, we describe experiments using colour spatiogram and block-based cross-correlation image features in conjunction with accelerometer sensor readings to cluster a day's worth of data into meaningful events, allowing the user to quickly browse a day's captured images. Two different low-complexity algorithms are detailed and evaluated for SenseCam image clustering
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