| |
Records |
Links |
|
Author |
Fei Yang; Luis Herranz; Joost Van de Weijer; Jose Antonio Iglesias; Antonio Lopez; Mikhail Mozerov |
|
| |
Title |
Variable Rate Deep Image Compression with Modulated Autoencoder |
Type |
Journal Article |
| |
Year |
2020 |
Publication |
IEEE Signal Processing Letters |
Abbreviated Journal |
SPL |
|
| |
Volume |
27 |
Issue |
|
Pages  |
331-335 |
|
| |
Keywords |
|
|
| |
Abstract |
Variable rate is a requirement for flexible and adaptable image and video compression. However, deep image compression methods (DIC) are optimized for a single fixed rate-distortion (R-D) tradeoff. While this can be addressed by training multiple models for different tradeoffs, the memory requirements increase proportionally to the number of models. Scaling the bottleneck representation of a shared autoencoder can provide variable rate compression with a single shared autoencoder. However, the R-D performance using this simple mechanism degrades in low bitrates, and also shrinks the effective range of bitrates. To address these limitations, we formulate the problem of variable R-D optimization for DIC, and propose modulated autoencoders (MAEs), where the representations of a shared autoencoder are adapted to the specific R-D tradeoff via a modulation network. Jointly training this modulated autoencoder and the modulation network provides an effective way to navigate the R-D operational curve. Our experiments show that the proposed method can achieve almost the same R-D performance of independent models with significantly fewer parameters. |
|
| |
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; ADAS; 600.141; 600.120; 600.118 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ YHW2020 |
Serial |
3346 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Pedro Martins; Paulo Carvalho; Carlo Gatta |
|
| |
Title |
Context-aware features and robust image representations |
Type |
Journal Article |
| |
Year |
2014 |
Publication |
Journal of Visual Communication and Image Representation |
Abbreviated Journal |
JVCIR |
|
| |
Volume |
25 |
Issue |
2 |
Pages |
339-348 |
|
| |
Keywords |
|
|
| |
Abstract |
Local image features are often used to efficiently represent image content. The limited number of types of features that a local feature extractor responds to might be insufficient to provide a robust image representation. To overcome this limitation, we propose a context-aware feature extraction formulated under an information theoretic framework. The algorithm does not respond to a specific type of features; the idea is to retrieve complementary features which are relevant within the image context. We empirically validate the method by investigating the repeatability, the completeness, and the complementarity of context-aware features on standard benchmarks. In a comparison with strictly local features, we show that our context-aware features produce more robust image representations. Furthermore, we study the complementarity between strictly local features and context-aware ones to produce an even more robust representation. |
|
| |
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; 600.079;MILAB |
Approved |
no |
|
| |
Call Number |
Admin @ si @ MCG2014 |
Serial |
2467 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Lu Yu; Lichao Zhang; Joost Van de Weijer; Fahad Shahbaz Khan; Yongmei Cheng; C. Alejandro Parraga |
|
| |
Title |
Beyond Eleven Color Names for Image Understanding |
Type |
Journal Article |
| |
Year |
2018 |
Publication |
Machine Vision and Applications |
Abbreviated Journal |
MVAP |
|
| |
Volume |
29 |
Issue |
2 |
Pages |
361-373 |
|
| |
Keywords |
Color name; Discriminative descriptors; Image classification; Re-identification; Tracking |
|
| |
Abstract |
Color description is one of the fundamental problems of image understanding. One of the popular ways to represent colors is by means of color names. Most existing work on color names focuses on only the eleven basic color terms of the English language. This could be limiting the discriminative power of these representations, and representations based on more color names are expected to perform better. However, there exists no clear strategy to choose additional color names. We collect a dataset of 28 additional color names. To ensure that the resulting color representation has high discriminative power we propose a method to order the additional color names according to their complementary nature with the basic color names. This allows us to compute color name representations with high discriminative power of arbitrary length. In the experiments we show that these new color name descriptors outperform the existing color name descriptor on the task of visual tracking, person re-identification and image classification. |
|
| |
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; NEUROBIT; 600.068; 600.109; 600.120 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ YYW2018 |
Serial |
3087 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Luis Herranz; Shuqiang Jiang; Ruihan Xu |
|
| |
Title |
Modeling Restaurant Context for Food Recognition |
Type |
Journal Article |
| |
Year |
2017 |
Publication |
IEEE Transactions on Multimedia |
Abbreviated Journal |
TMM |
|
| |
Volume |
19 |
Issue |
2 |
Pages |
430 - 440 |
|
| |
Keywords |
|
|
| |
Abstract |
Food photos are widely used in food logs for diet monitoring and in social networks to share social and gastronomic experiences. A large number of these images are taken in restaurants. Dish recognition in general is very challenging, due to different cuisines, cooking styles, and the intrinsic difficulty of modeling food from its visual appearance. However, contextual knowledge can be crucial to improve recognition in such scenario. In particular, geocontext has been widely exploited for outdoor landmark recognition. Similarly, we exploit knowledge about menus and location of restaurants and test images. We first adapt a framework based on discarding unlikely categories located far from the test image. Then, we reformulate the problem using a probabilistic model connecting dishes, restaurants, and locations. We apply that model in three different tasks: dish recognition, restaurant recognition, and location refinement. Experiments on six datasets show that by integrating multiple evidences (visual, location, and external knowledge) our system can boost the performance in all tasks. |
|
| |
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 @ HJX2017 |
Serial |
2965 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Bogdan Raducanu; Fadi Dornaika |
|
| |
Title |
Embedding new observations via sparse-coding for non-linear manifold learning |
Type |
Journal Article |
| |
Year |
2014 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
|
| |
Volume |
47 |
Issue |
1 |
Pages |
480-492 |
|
| |
Keywords |
|
|
| |
Abstract |
Non-linear dimensionality reduction techniques are affected by two critical aspects: (i) the design of the adjacency graphs, and (ii) the embedding of new test data-the out-of-sample problem. For the first aspect, the proposed solutions, in general, were heuristically driven. For the second aspect, the difficulty resides in finding an accurate mapping that transfers unseen data samples into an existing manifold. Past works addressing these two aspects were heavily parametric in the sense that the optimal performance is only achieved for a suitable parameter choice that should be known in advance. In this paper, we demonstrate that the sparse representation theory not only serves for automatic graph construction as shown in recent works, but also represents an accurate alternative for out-of-sample embedding. Considering for a case study the Laplacian Eigenmaps, we applied our method to the face recognition problem. To evaluate the effectiveness of the proposed out-of-sample embedding, experiments are conducted using the K-nearest neighbor (KNN) and Kernel Support Vector Machines (KSVM) classifiers on six public face datasets. The experimental results show that the proposed model is able to achieve high categorization effectiveness as well as high consistency with non-linear embeddings/manifolds obtained in batch modes. |
|
| |
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; |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RaD2013b |
Serial |
2316 |
|
| Permanent link to this record |
