| |
Records |
Links |
|
Author |
Xinhang Song; Shuqiang Jiang; Luis Herranz |
|
| |
Title |
Multi-Scale Multi-Feature Context Modeling for Scene Recognition in the Semantic Manifold |
Type |
Journal Article |
| |
Year |
2017 |
Publication |
IEEE Transactions on Image Processing |
Abbreviated Journal |
TIP |
|
| |
Volume |
26 |
Issue |
6 |
Pages |
2721-2735 |
|
| |
Keywords |
|
|
| |
Abstract |
Before the big data era, scene recognition was often approached with two-step inference using localized intermediate representations (objects, topics, and so on). One of such approaches is the semantic manifold (SM), in which patches and images are modeled as points in a semantic probability simplex. Patch models are learned resorting to weak supervision via image labels, which leads to the problem of scene categories co-occurring in this semantic space. Fortunately, each category has its own co-occurrence patterns that are consistent across the images in that category. Thus, discovering and modeling these patterns are critical to improve the recognition performance in this representation. Since the emergence of large data sets, such as ImageNet and Places, these approaches have been relegated in favor of the much more powerful convolutional neural networks (CNNs), which can automatically learn multi-layered representations from the data. In this paper, we address many limitations of the original SM approach and related works. We propose discriminative patch representations using neural networks and further propose a hybrid architecture in which the semantic manifold is built on top of multiscale CNNs. Both representations can be computed significantly faster than the Gaussian mixture models of the original SM. To combine multiple scales, spatial relations, and multiple features, we formulate rich context models using Markov random fields. To solve the optimization problem, we analyze global and local approaches, where a top-down hierarchical algorithm has the best performance. Experimental results show that exploiting different types of contextual relations jointly consistently improves the recognition accuracy. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
LAMP; 600.120 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ SJH2017a |
Serial  |
2963 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Mikhail Mozerov; Joost Van de Weijer |
|
| |
Title |
Improved Recursive Geodesic Distance Computation for Edge Preserving Filter |
Type |
Journal Article |
| |
Year |
2017 |
Publication |
IEEE Transactions on Image Processing |
Abbreviated Journal |
TIP |
|
| |
Volume |
26 |
Issue |
8 |
Pages |
3696 - 3706 |
|
| |
Keywords |
Geodesic distance filter; color image filtering; image enhancement |
|
| |
Abstract |
All known recursive filters based on the geodesic distance affinity are realized by two 1D recursions applied in two orthogonal directions of the image plane. The 2D extension of the filter is not valid and has theoretically drawbacks, which lead to known artifacts. In this paper, a maximum influence propagation method is proposed to approximate the 2D extension for the
geodesic distance-based recursive filter. The method allows to partially overcome the drawbacks of the 1D recursion approach. We show that our improved recursion better approximates the true geodesic distance filter, and the application of this improved filter for image denoising outperforms the existing recursive implementation of the geodesic distance. As an application,
we consider a geodesic distance-based filter for image denoising.
Experimental evaluation of our denoising method demonstrates comparable and for several test images better results, than stateof-the-art approaches, while our algorithm is considerably fasterwith computational complexity O(8P). |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
LAMP; ISE; 600.120; 600.098; 600.119 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ Moz2017 |
Serial |
2921 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Pedro Martins; Paulo Carvalho; Carlo Gatta |
|
| |
Title |
On the completeness of feature-driven maximally stable extremal regions |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
|
| |
Volume |
74 |
Issue |
|
Pages |
9-16 |
|
| |
Keywords |
Local features; Completeness; Maximally Stable Extremal Regions |
|
| |
Abstract |
By definition, local image features provide a compact representation of the image in which most of the image information is preserved. This capability offered by local features has been overlooked, despite being relevant in many application scenarios. In this paper, we analyze and discuss the performance of feature-driven Maximally Stable Extremal Regions (MSER) in terms of the coverage of informative image parts (completeness). This type of features results from an MSER extraction on saliency maps in which features related to objects boundaries or even symmetry axes are highlighted. These maps are intended to be suitable domains for MSER detection, allowing this detector to provide a better coverage of informative image parts. Our experimental results, which were based on a large-scale evaluation, show that feature-driven MSER have relatively high completeness values and provide more complete sets than a traditional MSER detection even when sets of similar cardinality are considered. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Elsevier B.V. |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
0167-8655 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
LAMP;MILAB; |
Approved |
no |
|
| |
Call Number |
Admin @ si @ MCG2016 |
Serial |
2748 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Adriana Romero; Carlo Gatta; Gustavo Camps-Valls |
|
| |
Title |
Unsupervised Deep Feature Extraction for Remote Sensing Image Classification |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
IEEE Transaction on Geoscience and Remote Sensing |
Abbreviated Journal |
TGRS |
|
| |
Volume |
54 |
Issue |
3 |
Pages |
1349 - 1362 |
|
| |
Keywords |
|
|
| |
Abstract |
This paper introduces the use of single-layer and deep convolutional networks for remote sensing data analysis. Direct application to multi- and hyperspectral imagery of supervised (shallow or deep) convolutional networks is very challenging given the high input data dimensionality and the relatively small amount of available labeled data. Therefore, we propose the use of greedy layerwise unsupervised pretraining coupled with a highly efficient algorithm for unsupervised learning of sparse features. The algorithm is rooted on sparse representations and enforces both population and lifetime sparsity of the extracted features, simultaneously. We successfully illustrate the expressive power of the extracted representations in several scenarios: classification of aerial scenes, as well as land-use classification in very high resolution or land-cover classification from multi- and hyperspectral images. The proposed algorithm clearly outperforms standard principal component analysis (PCA) and its kernel counterpart (kPCA), as well as current state-of-the-art algorithms of aerial classification, while being extremely computationally efficient at learning representations of data. Results show that single-layer convolutional networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels and are preferred when the classification requires high resolution and detailed results. However, deep architectures significantly outperform single-layer variants, capturing increasing levels of abstraction and complexity throughout the feature hierarchy. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
0196-2892 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
LAMP; 600.079;MILAB |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RGC2016 |
Serial |
2723 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Mikhail Mozerov; Joost Van de Weijer |
|
| |
Title |
Global Color Sparseness and a Local Statistics Prior for Fast Bilateral Filtering |
Type |
Journal Article |
| |
Year |
2015 |
Publication |
IEEE Transactions on Image Processing |
Abbreviated Journal |
TIP |
|
| |
Volume |
24 |
Issue |
12 |
Pages |
5842-5853 |
|
| |
Keywords |
|
|
| |
Abstract |
The property of smoothing while preserving edges makes the bilateral filter a very popular image processing tool. However, its non-linear nature results in a computationally costly operation. Various works propose fast approximations to the bilateral filter. However, the majority does not generalize to vector input as is the case with color images. We propose a fast approximation to the bilateral filter for color images. The filter is based on two ideas. First, the number of colors, which occur in a single natural image, is limited. We exploit this color sparseness to rewrite the initial non-linear bilateral filter as a number of linear filter operations. Second, we impose a statistical prior to the image values that are locally present within the filter window. We show that this statistical prior leads to a closed-form solution of the bilateral filter. Finally, we combine both ideas into a single fast and accurate bilateral filter for color images. Experimental results show that our bilateral filter based on the local prior yields an extremely fast bilateral filter approximation, but with limited accuracy, which has potential application in real-time video filtering. Our bilateral filter, which combines color sparseness and local statistics, yields a fast and accurate bilateral filter approximation and obtains the state-of-the-art results. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1057-7149 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
LAMP; 600.079;ISE |
Approved |
no |
|
| |
Call Number |
Admin @ si @ MoW2015b |
Serial |
2689 |
|
| Permanent link to this record |
