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Learning to Detect A Salient Object

by Tie Liu, Jian Sun, Nan-ning Zheng, Xiaoou Tang, Heung-yeung Shum - In CVPR , 2007
"... Abstract We study visual attention by detecting a salient object in an input image. We formulate salient object detection as an image segmentation problem, where we separate the salient object from the image background. We propose a set of novel features including multi-scale contrast, centersurroun ..."
Abstract - Cited by 240 (12 self) - Add to MetaCart
Abstract We study visual attention by detecting a salient object in an input image. We formulate salient object detection as an image segmentation problem, where we separate the salient object from the image background. We propose a set of novel features including multi-scale contrast

Compact Hashing for Mixed Image-Keyword Query over Multi-Label Images

by Xianglong Liu, Yadong Mu, Bo Lang, Shih-fu Chang
"... Recently locality-sensitive hashing (LSH) algorithms have attracted much attention owing to its empirical success and theoretic guarantee in large-scale visual search. In this paper we address the new topic of hashing with multi-label data, in which images in the database are assumed to be associate ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
Recently locality-sensitive hashing (LSH) algorithms have attracted much attention owing to its empirical success and theoretic guarantee in large-scale visual search. In this paper we address the new topic of hashing with multi-label data, in which images in the database are assumed

Compact Hashing for Mixed Image-Keyword Query over Multi-Label Images

by unknown authors
"... Recently locality-sensitive hashing (LSH) algorithms have attracted much attention owing to its empirical success and theoretic guarantee in large-scale visual search. In this pa-per we address the new topic of hashing with multi-label data, in which images in the database are assumed to be associat ..."
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Recently locality-sensitive hashing (LSH) algorithms have attracted much attention owing to its empirical success and theoretic guarantee in large-scale visual search. In this pa-per we address the new topic of hashing with multi-label data, in which images in the database are assumed

Learning Compact Visual Attributes for Large-scale Image Classification

by Yu Su, Frédéric Jurie
"... Abstract. Attributes based image classification has received a lot of attention recently, as an interesting tool to share knowledge across different categories or to produce compact signature of images. However, when high classification performance is expected, state-of-the-art results are typically ..."
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are typically obtained by combining Fisher Vectors (FV) and Spatial Pyramid Matching (SPM), leading to image signatures with dimensionality up to 262,144 [1]. This is a hindrance to large-scale image classification tasks, for which the attribute based approaches would be more efficient. This paper proposes a

Dissertation TOPIC MODELS FOR IMAGE RETRIEVAL ON LARGE-SCALE DATABASES

by Eva Hörster
"... With the explosion of the number of images in personal and on-line collections, efficient techniques for navigating, indexing, labeling and searching images become more and more important. In this work we will rely on the image content as the main source of information to retrieve images. We study t ..."
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we present a complete image retrieval system based on topic models and evaluate the suitability of different types of topic models for the task of large-scale retrieval on real-world databases. Different similarity measure are evaluated in a retrieval-by-example task. Next, we focus

Fusing Local Image Descriptors for Large-Scale Image Retrieval

by Eva Hörster, Rainer Lienhart , 2007
"... Online image repositories such as Flickr contain hundreds of millions of images and are growing quickly. Along with that the needs for supporting indexing, searching and browsing is becoming more and more pressing. Here we will employ the image content as a source of information to retrieve images a ..."
Abstract - Cited by 3 (0 self) - Add to MetaCart
and study the representation of images by topic models for content-based image retrieval. We focus on incorporating different types of visual descriptors into the topic modeling context. Three different fusion approaches are explored. The image representations for each fusion approach are learned

Discriminative codeword selection for image representation

by Lijun Zhang, Zhengguang Chen, Chun Chen, Shulong Tan, Jiajun Bu, Xiaofei He - in: Proceedings of the 18th ACM International Conference on Multimedia, 2010
"... Bag of features (BoF) representation has attracted an increasing amount of attention in large scale image processing systems. BoF representation treats images as loose collections of local invariant descriptors extracted from them. The visual codebook is generally constructed by using an unsupervise ..."
Abstract - Cited by 6 (2 self) - Add to MetaCart
Bag of features (BoF) representation has attracted an increasing amount of attention in large scale image processing systems. BoF representation treats images as loose collections of local invariant descriptors extracted from them. The visual codebook is generally constructed by using

Decomposition, discovery and detection of visual categories using topic models

by Mario Fritz, Bernt Schiele - In: CVPR , 2008
"... We present a novel method for the discovery and detection of visual object categories based on decompositions using topic models. The approach is capable of learning a compact and low dimensional representation for multiple visual categories from multiple view points without labeling of the training ..."
Abstract - Cited by 54 (5 self) - Add to MetaCart
We present a novel method for the discovery and detection of visual object categories based on decompositions using topic models. The approach is capable of learning a compact and low dimensional representation for multiple visual categories from multiple view points without labeling

Thesis Advisor Accepted by.......Gakenhimer..

by Gregory A. Rossel, Fiichael J. Shiffer, Ralph Gakenheimer, Gregory A. Rossel
"... ussRaNIte ..."
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Abstract not found

Heterogeneous Image Features Integration via Multi-Modal Semi-Supervised Learning Model

by unknown authors
"... Automatic image categorization has become increas-ingly important with the development of Internet and the growth in the size of image databases. Although the im-age categorization can be formulated as a typical multi-class classification problem, two major challenges have been raised by the real-wo ..."
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and different fea-tures describe different aspects of the visual characteristics. Therefore, how to integrate heterogeneous visual features to do the semi-supervised learning is crucial for categorizing large-scale image data. In this paper, we propose a novel approach to integrate heterogeneous features
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