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

Presents a theoretically very simple, yet efficient, multiresolution approach to gray-scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing that certain local binary patterns, termed "uniform," are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature. We derive a generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis. The proposed approach is very robust in terms of gray-scale variations since the operator is, by definition, invariant against any monotonic transformation of the gray scale. Another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table. Experimental results demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns.

/pdf/multiresolution-gray-scale-and-rotation-invariant-texture-1b1y2fta4g.pdf

Multiresolution gray-scale and rotation invariant texture classification with local binary patterns

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator. We show that this model can generate MNIST digits conditioned on class labels. We also illustrate how this model could be used to learn a multi-modal model, and provide preliminary examples of an application to image tagging in which we demonstrate how this approach can generate descriptive tags which are not part of training labels.

Conditional Generative Adversarial Nets

Presents a review of 200 references in content-based image retrieval. The paper starts with discussing the working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap. Subsequent sections discuss computational steps for image retrieval systems. Step one of the review is image processing for retrieval sorted by color, texture, and local geometry. Features for retrieval are discussed next, sorted by: accumulative and global features, salient points, object and shape features, signs, and structural combinations thereof. Similarity of pictures and objects in pictures is reviewed for each of the feature types, in close connection to the types and means of feedback the user of the systems is capable of giving by interaction. We briefly discuss aspects of system engineering: databases, system architecture, and evaluation. In the concluding section, we present our view on: the driving force of the field, the heritage from computer vision, the influence on computer vision, the role of similarity and of interaction, the need for databases, the problem of evaluation, and the role of the semantic gap.

/pdf/content-based-image-retrieval-at-the-end-of-the-early-years-q45iy9ejai.pdf

Content-based image retrieval at the end of the early years

https://scholarlypublications.universiteitleiden.nl/access/item%3A2943283/view

Deep learning for visual understanding

Extending beyond the boundaries of science, art, and culture, content-based multimedia information retrieval provides new paradigms and methods for searching through the myriad variety of media all over the world. This survey reviews 100p recent articles on content-based multimedia information retrieval and discusses their role in current research directions which include browsing and search paradigms, user studies, affective computing, learning, semantic queries, new features and media types, high performance indexing, and evaluation techniques. Based on the current state of the art, we discuss the major challenges for the future.

/pdf/content-based-multimedia-information-retrieval-state-of-the-41z647ll8w.pdf

Content-based multimedia information retrieval: State of the art and challenges

In most well known image retrieval test sets, the imagery typically cannot be freely distributed or is not representative of a large community of users. In this paper we present a collection for the MIR community comprising 25000 images from the Flickr website which are redistributable for research purposes and represent a real community of users both in the image content and image tags. We have extracted the tags and EXIF image metadata, and also make all of these publicly available. In addition we discuss several challenges for benchmarking retrieval and classification methods.

/pdf/the-mir-flickr-retrieval-evaluation-4j0o3wizx2.pdf

The MIR flickr retrieval evaluation

During the long history of computer vision, one of the grand challenges has been semantic segmentation which is the ability to segment an unknown image into different parts and objects (e.g., beach, ocean, sun, dog, swimmer). Furthermore, segmentation is even deeper than object recognition because recognition is not necessary for segmentation. Specifically, humans can perform image segmentation without even knowing what the objects are (for example, in satellite imagery or medical X-ray scans, there may be several objects which are unknown, but they can still be segmented within the image typically for further investigation). Performing segmentation without knowing the exact identity of all objects in the scene is an important part of our visual understanding process which can give us a powerful model to understand the world and also be used to improve or augment existing computer vision techniques. Herein this work, we review the field of semantic segmentation as pertaining to deep convolutional neural networks. We provide comprehensive coverage of the top approaches and summarize the strengths, weaknesses and major challenges.

/pdf/a-review-of-semantic-segmentation-using-deep-neural-networks-4b2hw28vl9.pdf

A review of semantic segmentation using deep neural networks

The MIR Flickr collection consists of 25000 high-quality photographic images of thousands of Flickr users, made available under the Creative Commons license. The database includes all the original user tags and EXIF metadata. Additionally, detailed and accurate annotations are provided for topics corresponding to the most prominent visual concepts in the user tag data. The rich metadata allow for a wide variety of image retrieval benchmarking scenarios.In this paper, we provide an overview of the various strategies that were devised for automatic visual concept detection using the MIR Flickr collection. In particular we discuss results from various experiments in combining social data and low-level content-based descriptors to improve the accuracy of visual concept classifiers. Additionally, we present retrieval results obtained by relevance feedback methods, demonstrating (i) how their performance can be enhanced using features based on visual concept classifiers, and (ii) how their performance, based on small samples, can be measured relative to their large sample classifier counterparts.Additionally, we identify a number of promising trends and ideas in visual concept detection. To keep the MIR Flickr collection up-to-date on these developments, we have formulated two new initiatives to extend the original image collection. First, the collection will be extended to one million Creative Commons Flickr images. Second, a number of state-of-the-art content-based descriptors will be made available for the entire collection.

/pdf/new-trends-and-ideas-in-visual-concept-detection-the-mir-48eg5rdlva.pdf

Michael S. Lew

Papers

Deep learning for visual understanding

Content-based multimedia information retrieval: State of the art and challenges

The MIR flickr retrieval evaluation

A review of semantic segmentation using deep neural networks

New trends and ideas in visual concept detection: the MIR flickr retrieval evaluation initiative