USEMP at MediaEval Placing Task 2014
نویسندگان
چکیده
We describe the participation of the USEMP team in the Placing Task at MediaEval 2014. We submitted four textual runs which are inspired by CEA LIST’s 2013 participation. Our entries are based on probabilistic place modeling but also exploit machine tag and/or user modeling. The best results were obtained when all these types of information are combined. The accuracy of automatic at 1km reaches 0.235 when using only training data provided by organizers and 0.441 with the use of external data.
منابع مشابه
USEMP: Finding Diverse Images at MediaEval 2015
We describe the participation of the USEMP team in the Retrieving Diverse Social Images Task of MediaEval 2015. Our runs are produced based on a supervised diversification method that jointly optimizes relevance and diversity. All runs are automated and use only resources given by the task organizers. Our best results in terms of the official ranking metric on the one-topic part of the test set...
متن کاملSocialSensor at MediaEval Placing Task 2014
We describe the participation of the SocialSensor team in the Placing Task of MediaEval 2014. We submitted three runs based on tag information for the full test set, using extensions over an existing language modelling approach, and two runs (one based on the full test set and the other on the 25,500 subset) based on visual content, using geospatial clustering and supervised-learning. Our best ...
متن کاملUQ-DKE's Participation at MediaEval 2014 Placing Task
In this paper, we describe our approach as part of the MediaEval 2014 Placing Task evaluation. We first identify tags that are most indicative of geographic location by calculating the spatial-aware weighting for all tags in the training set. These weighting are applied to a language model-based retrieval framework. To address the geo-tagging problem, we find the most similar training item and ...
متن کاملThe 2014 ICSI/TU Delft Location Estimation System
In this paper, we describe the ICSI/TU Delft video location estimation system presented at the MediaEval 2014 Placing Task. We describe two text-based approaches based on spatial variance and graphical model framework, a visualcontent-based geo-visual ranking approach, and a multi-modal approach that combines the text and visual-based algorithms.
متن کاملTALP-UPC at MediaEval 2014 Placing Task: Combining Geographical Knowledge Bases and Language Models for Large-Scale Textual Georeferencing
This paper describes our Georeferencing approaches, experiments, and results at the MediaEval 2014 Placing Task evaluation. The task consists of predicting the most probable geographical coordinates of Flickr images and videos using its visual, audio and metadata associated features. Our approaches used only Flickr users textual metadata annotations and tagsets. We used four approaches for this...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2014