121,202 research outputs found
Object Discovery From a Single Unlabeled Image by Mining Frequent Itemset With Multi-scale Features
TThe goal of our work is to discover dominant objects in a very general
setting where only a single unlabeled image is given. This is far more
challenge than typical co-localization or weakly-supervised localization tasks.
To tackle this problem, we propose a simple but effective pattern mining-based
method, called Object Location Mining (OLM), which exploits the advantages of
data mining and feature representation of pre-trained convolutional neural
networks (CNNs). Specifically, we first convert the feature maps from a
pre-trained CNN model into a set of transactions, and then discovers frequent
patterns from transaction database through pattern mining techniques. We
observe that those discovered patterns, i.e., co-occurrence highlighted
regions, typically hold appearance and spatial consistency. Motivated by this
observation, we can easily discover and localize possible objects by merging
relevant meaningful patterns. Extensive experiments on a variety of benchmarks
demonstrate that OLM achieves competitive localization performance compared
with the state-of-the-art methods. We also evaluate our approach compared with
unsupervised saliency detection methods and achieves competitive results on
seven benchmark datasets. Moreover, we conduct experiments on fine-grained
classification to show that our proposed method can locate the entire object
and parts accurately, which can benefit to improving the classification results
significantly
Predicting Successful Memes using Network and Community Structure
We investigate the predictability of successful memes using their early
spreading patterns in the underlying social networks. We propose and analyze a
comprehensive set of features and develop an accurate model to predict future
popularity of a meme given its early spreading patterns. Our paper provides the
first comprehensive comparison of existing predictive frameworks. We categorize
our features into three groups: influence of early adopters, community
concentration, and characteristics of adoption time series. We find that
features based on community structure are the most powerful predictors of
future success. We also find that early popularity of a meme is not a good
predictor of its future popularity, contrary to common belief. Our methods
outperform other approaches, particularly in the task of detecting very popular
or unpopular memes.Comment: 10 pages, 6 figures, 2 tables. Proceedings of 8th AAAI Intl. Conf. on
Weblogs and social media (ICWSM 2014
Monitoring land use changes using geo-information : possibilities, methods and adapted techniques
Monitoring land use with geographical databases is widely used in decision-making. This report presents the possibilities, methods and adapted techniques using geo-information in monitoring land use changes. The municipality of Soest was chosen as study area and three national land use databases, viz. Top10Vector, CBS land use statistics and LGN, were used. The restrictions of geo-information for monitoring land use changes are indicated. New methods and adapted techniques improve the monitoring result considerably. Providers of geo-information, however, should coordinate on update frequencies, semantic content and spatial resolution to allow better possibilities of monitoring land use by combining data sets
ERTS-1 imagery use in reconnaissance prospecting: Evaluation of commercial utility of ERTS-1 imagery in structural reconnaissance for minerals and petroleum
The author has identified the following significant results. This study was performed to investigate applications of ERTS-1 imagery in commercial reconnaissance for mineral and hydrocarbon resources. ERTS-1 imagery collected over five areas in North America (Montana; Colorado; New Mexico-West Texas; Superior Province, Canada; and North Slope, Alaska) has been analyzed for data content including linears, lineaments, and curvilinear anomalies. Locations of these features were mapped and compared with known locations of mineral and hydrocarbon accumulations. Results were analyzed in the context of a simple-shear, block-coupling model. Data analyses have resulted in detection of new lineaments, some of which may be continental in extent, detection of many curvilinear patterns not generally seen on aerial photos, strong evidence of continental regmatic fracture patterns, and realization that geological features can be explained in terms of a simple-shear, block-coupling model. The conculsions are that ERTS-1 imagery is of great value in photogeologic/geomorphic interpretations of regional features, and the simple-shear, block-coupling model provides a means of relating data from ERTS imagery to structures that have controlled emplacement of ore deposits and hydrocarbon accumulations, thus providing a basis for a new approach for reconnaissance for mineral, uranium, gas, and oil deposits and structures
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