6,471 research outputs found
Holons Visual Representation for Image Retrieval
This work was supported in part by the National Natural Science Foundation of China under Grant 61370149, in part by the Fundamental Research Funds for the Central Universities under Grant ZYGX2013J083, by the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry under Grant LXHG42DL, by the National Basic Research Program of China under Grant 2013CB329305, by the National Natural Science Foundation of China under Grant 61422213, and by the Strategic Priority Research Program of the Chinese Academy of Sciences
Content Based Image Retrieval System Using NOHIS-tree
Content-based image retrieval (CBIR) has been one of the most important
research areas in computer vision. It is a widely used method for searching
images in huge databases. In this paper we present a CBIR system called
NOHIS-Search. The system is based on the indexing technique NOHIS-tree. The two
phases of the system are described and the performance of the system is
illustrated with the image database ImagEval. NOHIS-Search system was compared
to other two CBIR systems; the first that using PDDP indexing algorithm and the
second system is that using the sequential search. Results show that
NOHIS-Search system outperforms the two other systems.Comment: 6 pages, 10th International Conference on Advances in Mobile
Computing & Multimedia (MoMM2012
Large Graph Analysis in the GMine System
Current applications have produced graphs on the order of hundreds of
thousands of nodes and millions of edges. To take advantage of such graphs, one
must be able to find patterns, outliers and communities. These tasks are better
performed in an interactive environment, where human expertise can guide the
process. For large graphs, though, there are some challenges: the excessive
processing requirements are prohibitive, and drawing hundred-thousand nodes
results in cluttered images hard to comprehend. To cope with these problems, we
propose an innovative framework suited for any kind of tree-like graph visual
design. GMine integrates (a) a representation for graphs organized as
hierarchies of partitions - the concepts of SuperGraph and Graph-Tree; and (b)
a graph summarization methodology - CEPS. Our graph representation deals with
the problem of tracing the connection aspects of a graph hierarchy with sub
linear complexity, allowing one to grasp the neighborhood of a single node or
of a group of nodes in a single click. As a proof of concept, the visual
environment of GMine is instantiated as a system in which large graphs can be
investigated globally and locally
Context-aware person identification in personal photo collections
Identifying the people in photos is an important need for users of photo management systems. We present MediAssist, one such system which facilitates browsing, searching and semi-automatic annotation of personal photos, using analysis of both image content and the context in which the photo is captured. This semi-automatic annotation includes annotation of the identity of people in photos. In this paper, we focus on such person annotation, and propose person identification techniques based on a combination of context and content. We propose language modelling and nearest neighbor approaches to context-based person identification, in addition to novel face color and image color content-based features (used alongside face recognition and body patch features). We conduct a comprehensive empirical study of these techniques using the real private photo collections of a number of users, and show that combining context- and content-based analysis improves performance over content or context alone
Cultural Event Recognition with Visual ConvNets and Temporal Models
This paper presents our contribution to the ChaLearn Challenge 2015 on
Cultural Event Classification. The challenge in this task is to automatically
classify images from 50 different cultural events. Our solution is based on the
combination of visual features extracted from convolutional neural networks
with temporal information using a hierarchical classifier scheme. We extract
visual features from the last three fully connected layers of both CaffeNet
(pretrained with ImageNet) and our fine tuned version for the ChaLearn
challenge. We propose a late fusion strategy that trains a separate low-level
SVM on each of the extracted neural codes. The class predictions of the
low-level SVMs form the input to a higher level SVM, which gives the final
event scores. We achieve our best result by adding a temporal refinement step
into our classification scheme, which is applied directly to the output of each
low-level SVM. Our approach penalizes high classification scores based on
visual features when their time stamp does not match well an event-specific
temporal distribution learned from the training and validation data. Our system
achieved the second best result in the ChaLearn Challenge 2015 on Cultural
Event Classification with a mean average precision of 0.767 on the test set.Comment: Initial version of the paper accepted at the CVPR Workshop ChaLearn
Looking at People 201
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Hierarchical classification for multiple, distributed web databases
The proliferation of online information resources increases the importance of effective and efficient distributed searching. Our research aims to provide an alternative hierarchical categorization and search capability based on a Bayesian network learning algorithm. Our proposed approach, which is grounded on automatic textual analysis of subject content of online web databases, attempts to address the database selection problem by first classifying web databases into a hierarchy of topic categories. The experimental results reported demonstrate that such a classification approach not only effectively reduces the class search space, but also helps to significantly improve the accuracy of classification performance
Visual Search at eBay
In this paper, we propose a novel end-to-end approach for scalable visual
search infrastructure. We discuss the challenges we faced for a massive
volatile inventory like at eBay and present our solution to overcome those. We
harness the availability of large image collection of eBay listings and
state-of-the-art deep learning techniques to perform visual search at scale.
Supervised approach for optimized search limited to top predicted categories
and also for compact binary signature are key to scale up without compromising
accuracy and precision. Both use a common deep neural network requiring only a
single forward inference. The system architecture is presented with in-depth
discussions of its basic components and optimizations for a trade-off between
search relevance and latency. This solution is currently deployed in a
distributed cloud infrastructure and fuels visual search in eBay ShopBot and
Close5. We show benchmark on ImageNet dataset on which our approach is faster
and more accurate than several unsupervised baselines. We share our learnings
with the hope that visual search becomes a first class citizen for all large
scale search engines rather than an afterthought.Comment: To appear in 23rd SIGKDD Conference on Knowledge Discovery and Data
Mining (KDD), 2017. A demonstration video can be found at
https://youtu.be/iYtjs32vh4
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