There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

The measurement of meaning

The PASCAL Visual Object Classes Challenge

Sparse representation was originally used in signal processing as a
powerful tool for acquiring, representing and compressing
high-dimensional signals. Recently, motivated by the great successes it has
achieved, it has become a hot research topic in the domain
of computer vision and pattern recognition. In this paper, we 
propose to adapt sparse representation to the problem of Visual Object Categorization which aims at predicting whether at least one or several objects of some given categories are present in an image. Thus, we have elaborated a reconstructive and discriminative sparse
representation of images, which integrates a discriminative term, such as
Fisher discriminative measure or the output of a SVM classifier, into
the standard sparse representation objective function in order to
learn a reconstructive and discriminative dictionary.

Experiments carried out on the SIMPLIcity image dataset have clearly
revealed that our reconstructive and discriminative approach has gained an obvious
improvement of the classification accuracy compared to standard SVM
using image features as input. Moreover, the results have shown that our approach is more efficient than a sparse representation being only reconstructive, which indicates that adding a discriminative term for
constructing the sparse representation is more suitable for the
categorization purpose.

/pdf/reconstructive-and-discriminative-sparse-representation-for-544ltwrqz2.pdf

Reconstructive and Discriminative Sparse Representation for Visual Object Categorization

This paper describes OMNIA, a system and interface for searching in multimodal image collections. OMNIA includes a set of tools which allow the user to retrieve assets using different features. The tools are based on extracting different types of asset features, which are content, aesthetic, and emotion. Visual-based features are used to retrieve assets using each of these tools. In addition, text-based features can be used to retrieve image assets based on content. Different datasets are used in OMNIA and retrieved assets are displayed in such a way which facilitates user navigation. It is shown how OMNIA can be used for simple, efficient, and intuitive asset search in the context of graphic design applications.

Multimodal search for graphic designers

This paper presents a novel approach for visual object categorization using region based features and statistical measures based image modeling. Our region-based features are extracted from coarse regions obtained by the Gestalt theory inspired region segmentation algorithm and they capture visually signiﬁcant information such as segments and colors. The modeling of the visual content of an image relies upon some statistical measures over sparse region-based features, thus avoiding the major difﬁculty of the popular “bag-of-local features” approach which needs to ﬁx a visual vocabulary size. Several classiﬁcation schemes, including feature selection techniques (e.g. PCA or Adaboost) and fusion strategies, are also implemented and compared. Experimented on a subset of Pascal VOC dataset, we show that by separating features extracted from different sources in different “channels”, and then to combine them using an early fusion, we can actually improve classiﬁcation performance. Moreover, experimental results demonstrate that our region-based features can be combined with SIFT features to reinforce performance, suggesting that our features managed to extract information which is complementary to the one of SIFT features.

Visual object categorization based on the fusion of region and local features.

Oriented boundary detection is a challenging task aimed at both delineating category-agnostic object instances and inferring their spatial layout from a single RGB image. State-of-the-art deep convolutional networks for this task rely on two independent streams that predict boundaries and occlusions respectively, although both require similar local and global cues, and occlusions cause boundaries. We therefore propose a fully convolutional bicameral structuring, composed of two cascaded decoders sharing one deep encoder, linked altogether by skip connections to combine local and global features, for jointly predicting instance boundaries and their unoccluded side. Furthermore, state-of-the-art datasets contain real images with few instances and occlusions mostly due to objects occluding the background, thereby missing meaningful occlusions between instances. For evaluating the missing scenario of dense piles of objects as well, we introduce synthetic data (Mikado), which extensibly contains more instances and inter-instance occlusions per image than the PASCAL Instance Occlusion Dataset (PIOD), the COCO Amodal dataset (COCOA), and the Densely Segmented Supermarket Amodal dataset (D2SA). We show that the proposed network design outperforms the two-stream baseline and alternative archiectures for oriented boundary detection on both PIOD and Mikado, and the amodal segmentation approach on COCOA as well. Our experiments on D2SA also show that Mikado is plausible in the sense that it enables the learning of performance-enhancing representations transferable to real data, while drastically reducing the need of hand-made annotations for finetuning.

https://hal.archives-ouvertes.fr/hal-02151828/document

Bicameral Structuring and Synthetic Imagery for Jointly Predicting Instance Boundaries and Nearby Occlusions from a Single Image.

Since the challenging visual object categorization has attracted more and more attention in recent years, we present in this paper a novel approach called statistical measures based image modeling for this problem, thus avoiding the major difficulty of the popular “bag-of-visual words” approach which needs to fix a visual vocabulary size. We use a series of statistical measures over our proper region based color and segment features as well as the popular SIFT, extracted from an image, to model its visual content. Then this new image modeling will be fed to a certain classifier to accomplish the object categorization task. Several classification schemes combined with some feature selection techniques and fusion strategies have also been implemented and compared within the experimentation carried out on a subset of Pascal VOC dataset. The results show that merging the region based features and SIFT which are from different sources using an early fusion can actually improve classification performance, suggesting that these features managed to extract information which is complementary to each other.

Emmanuel Dellandréa

Papers

Reconstructive and Discriminative Sparse Representation for Visual Object Categorization

Multimodal search for graphic designers

Visual object categorization based on the fusion of region and local features.

Bicameral Structuring and Synthetic Imagery for Jointly Predicting Instance Boundaries and Nearby Occlusions from a Single Image.

Image modeling using statistical measures for visual object categorization