Deep Convolutional Networks for Scene Parsing
نویسنده
چکیده
We propose a deep learning strategy for scene parsing, i.e. to asssign a class label to each pixel of an image. We investigate the use of deep convolutional network for modeling the complex scene label structures, relying on a supervised greedy learning strategy. Compared to standard approaches based on CRFs, our strategy does not need hand-crafted features, allows modeling more complex spatial dependencies and has a lower inference cost. Experiments over the MSRC benchmark and the LabelMe dataset show the effectiveness of our approach.
منابع مشابه
Deep Deconvolutional Networks for Scene Parsing
Scene parsing is an important and challenging problem in computer vision. It requires labeling each pixel in an image with the category it belongs to. Traditionally, it has been approached with hand-engineered features from color information in images. Recently convolutional neural networks (CNNs), which automatically learn hierarchies of features, have achieved record performance on the task. ...
متن کاملScene parsing using inference Embedded Deep Networks
Effective features and graphical model are two key points for realizing high performance scene parsing. Recently, Convolutional Neural Networks (CNNs) have shown great ability of learning features and attained remarkable performance. However, most researches use CNNs and graphical model separately, and do not exploit full advantages of both methods. In order to achieve better performance, this ...
متن کاملScene Parsing by Weakly Supervised Learning with Image Descriptions
This paper investigates a fundamental problem of scene understanding: how to parse a scene image into a structured configuration (i.e., a semantic object hierarchy with object interaction relations). We propose a deep architecture consisting of two networks: i) a convolutional neural network (CNN) extracting the image representation for pixel-wise object labeling and ii) a recursive neural netw...
متن کاملDeep Image Features in Music Information Retrieval
Applications of Convolutional Neural Networks (CNNs) to various problems have been the subject of a number of recent studies ranging from image classification and object detection to scene parsing, segmentation 3D volumetric images and action recognition in videos. CNNs are able to learn input data representation, instead of using fixed engineered features. In this study, the image model traine...
متن کاملEstimation of Hand Skeletal Postures by Using Deep Convolutional Neural Networks
Hand posture estimation attracts researchers because of its many applications. Hand posture recognition systems simulate the hand postures by using mathematical algorithms. Convolutional neural networks have provided the best results in the hand posture recognition so far. In this paper, we propose a new method to estimate the hand skeletal posture by using deep convolutional neural networks. T...
متن کامل