Results 1 - 10
of
101
Rich feature hierarchies for accurate object detection and semantic segmentation
"... Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex en-semble systems that typically combine multiple low-level image features with high-level context. In this paper, we propose a simple and scala ..."
Abstract
-
Cited by 251 (23 self)
- Add to MetaCart
in order to localize and segment objects and (2) when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost. Since we combine region proposals with CNNs, we call our method R-CNN: Regions with CNN
Supervised learning of semantic classes for image annotation and retrieval
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2007
"... Abstract—A probabilistic formulation for semantic image annotation and retrieval is proposed. Annotation and retrieval are posed as classification problems where each class is defined as the group of database images labeled with a common semantic label. It is shown that, by establishing this one-to- ..."
Abstract
-
Cited by 223 (18 self)
- Add to MetaCart
and extensive experiments. The supervised formulation is shown to achieve higher accuracy than various previously published methods at a fraction of their computational cost. Finally, the proposed method is shown to be fairly robust to parameter tuning. Index Terms—Content-based image retrieval, semantic image
Self-Tuning Semantic Image Segmentation
"... Abstract. In this paper we present a method for finding optimal parameters of graph Laplacian-based semantic segmentation. This method is fully unsupervised and provides parameters individually for each image. In the experiments on Graz dataset the accuracy of segmentation obtained with the paramete ..."
Abstract
- Add to MetaCart
Abstract. In this paper we present a method for finding optimal parameters of graph Laplacian-based semantic segmentation. This method is fully unsupervised and provides parameters individually for each image. In the experiments on Graz dataset the accuracy of segmentation obtained
Fully convolutional networks for semantic segmentation
, 2014
"... Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolu-tional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmen-tation. Our key insight is to build “fully convolutional” networks that take ..."
Abstract
-
Cited by 37 (0 self)
- Add to MetaCart
classification networks (AlexNet [17], the VGG net [28], and GoogLeNet [29]) into fully convolu-tional networks and transfer their learned representations by fine-tuning [2] to the segmentation task. We then de-fine a novel architecture that combines semantic informa-tion from a deep, coarse layer
Locating tune changes and providing a semantic labelling of sets of Irish traditional tunes
- Proceedings of the International Symposium on Music Information Retrieval
, 2010
"... An approach is presented which provides the tune change locations within a set of Irish Traditional tunes. Also provided are semantic labels for each part of each tune within the set. A set in Irish Traditional music is a number of individual tunes played segue. Each of the tunes in the set are made ..."
Abstract
-
Cited by 3 (1 self)
- Add to MetaCart
locations and semantic part labels. Figure 1. A representation of the structure present within a piece of Irish Traditional music. There are two distinct hierarchical levels of segmentation. The piece of music consists of segments called tunes, and each tune consists of further segments called parts. 1.
Weakly Supervised Semantic Segmentation with Convolutional Networks
"... We are interested in inferring object segmentation by leveraging only object class information, and by consider-ing only minimal priors on the object segmentation task. This problem could be viewed as a kind of weakly super-vised segmentation task, and naturally fits the Multiple In-stance Learning ..."
Abstract
- Add to MetaCart
, by adding only few smoothing priors. Our system is trained using a subset of the Imagenet dataset and the segmentation experiments are performed on the challenging Pascal VOC dataset (with no fine-tuning of the model on Pascal VOC). Our model beats the state of the art results in weakly supervised object
A Comparison of Geometric and Energy-Based Point Cloud Semantic Segmentation Methods
"... Abstract—The recent availability of inexpensive RGB-D cameras, such as the Microsoft Kinect, has raised interest in the robotics community for point cloud segmentation. We are interested in the semantic segmentation task in which the goal is to find some relevant classes for navigation, wall, ground ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
Abstract—The recent availability of inexpensive RGB-D cameras, such as the Microsoft Kinect, has raised interest in the robotics community for point cloud segmentation. We are interested in the semantic segmentation task in which the goal is to find some relevant classes for navigation, wall
SEMANTIC LABELING OF TRACK EVENTS USING TIME SERIES SEGMENTATION AND SHAPE ANALYSIS
"... This paper presents a novel framework for applying semantic labels to events within a track. A track is a two-dimensional (2D) or a three-dimensional (3D) signal in time where each point of the signal is the x and y (and z) centroid spatial coordinate of an object at a specific frame of the video. T ..."
Abstract
- Add to MetaCart
This paper presents a novel framework for applying semantic labels to events within a track. A track is a two-dimensional (2D) or a three-dimensional (3D) signal in time where each point of the signal is the x and y (and z) centroid spatial coordinate of an object at a specific frame of the video
Research Track Paper From Frequent Itemsets to Semantically Meaningful Visual Patterns
"... Data mining techniques that are successful in transaction and text data may not be simply applied to image data that contain high-dimensional features and have spatial structures. It is not a trivial task to discover meaningful visual patterns in image databases, because the content variations and s ..."
Abstract
- Add to MetaCart
to the discovery of meaningful itemsets based on frequent itemset mining; (2) a self-supervised clustering scheme of the high-dimensional visual features by feeding back discovered patterns to tune the similarity measure through metric learning; and (3) a pattern summarization method that deals
Research Track Paper From Frequent Itemsets to Semantically Meaningful Visual Patterns
"... Data mining techniques that are successful in transaction and text data may not be simply applied to image data that contain high-dimensional features and have spatial structures. It is not a trivial task to discover meaningful visual patterns in image databases, because the content variations and s ..."
Abstract
- Add to MetaCart
to the discovery of meaningful itemsets based on frequent itemset mining; (2) a self-supervised clustering scheme of the high-dimensional visual features by feeding back discovered patterns to tune the similarity measure through metric learning; and (3) a pattern summarization method that deals
Results 1 - 10
of
101