1,075 research outputs found
A look inside the Pl@ntNet experience
International audiencePl@ntNet is an innovative participatory sensing platform relying on image-based plants identification as a mean to enlist non-expert contributors and facilitate the production of botanical observation data. One year after the public launch of the mobile application, we carry out a self-critical evaluation of the experience with regard to the requirements of a sustainable and effective ecological surveillance tool. We first demonstrate the attractiveness of the developed multimedia system (with more than 90K end-users) and the nice self-improving capacities of the whole collaborative workflow. We then point out the current limitations of the approach towards producing timely and accurate distribution maps of plants at a very large scale. We discuss in particular two main issues: the bias and the incompleteness of the produced data. We finally open new perspectives and describe upcoming realizations towards bridging these gaps
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SEARCHING BASED ON QUERY DOCUMENTS
Searches can start with query documents where search queries are formulated based on document-level descriptions. This type of searches is more common in domain-specific search environments. For example, in patent retrieval, one major search task is finding relevant information for new (query) patents, and search queries are generated from the query patents One unique characteristic of this search is that the search process can take longer and be more comprehensive, compared to general web search. As an example, to complete a single patent retrieval task, a typical user may generate 15 queries and examine more than 100 retrieved documents. In these search environments, searchers need to formulate multiple queries based on query documents that are typically complex and difficult to understand. In this work, we describe methods for automatically generating queries and diversifying search results based on query documents, which can be used for query vi suggestion and for improving the quality of retrieval results. In particular, we focus on resolving three main issues related to query document-based searches: (1) query generation, (2) query suggestion and formulation, and (3) search result diversification. Automatic query generation helps users by reducing the burden of formulating queries from query documents. Using generated queries as suggestions is investigated as a method of presenting alternative queries. Search result diversification is important in domain-specific search because of the nature of the query documents. Since query documents generally contain long complex descriptions, diverse query topics can be identified, and a range of relevant documents can be found that are related to these diverse topics. The proposed methods we study in this thesis explicitly address these three issues. To solve the query generation issue, we use binary decision trees to generate effective Boolean queries and labeling propagation to formulate more effective phrasal-concept queries. In order to diversify search results, we propose two different approaches: query-side and result-level diversification. To generate diverse queries, we identify important topics from query documents and generate queries based on the identified topics. For result-level diversification, we extract query topics from query documents, and apply state-of-the-art diversification algorithms based on the extracted topics. In addition, we devise query suggestion techniques for each query generation method. To demonstrate the effectiveness of our approach, we conduct experiments for various domain-specific search tasks, and devise appropriate evaluation measures for domain-specific search environments
On construction, performance, and diversification for structured queries on the semantic desktop
[no abstract
Automatic detection of geospatial objects using multiple hierarchical segmentations
Cataloged from PDF version of article.The object-based analysis of remotely sensed imagery
provides valuable spatial and structural information that
is complementary to pixel-based spectral information in classi-
fication. In this paper, we present novel methods for automatic
object detection in high-resolution images by combining spectral
information with structural information exploited by using
image segmentation. The proposed segmentation algorithm uses
morphological operations applied to individual spectral bands
using structuring elements in increasing sizes. These operations
produce a set of connected components forming a hierarchy of
segments for each band. A generic algorithm is designed to select
meaningful segments that maximize a measure consisting
of spectral homogeneity and neighborhood connectivity. Given
the observation that different structures appear more clearly at
different scales in different spectral bands, we describe a new
algorithm for unsupervised grouping of candidate segments belonging
to multiple hierarchical segmentations to find coherent
sets of segments that correspond to actual objects. The segments
are modeled by using their spectral and textural content, and
the grouping problem is solved by using the probabilistic latent
semantic analysis algorithm that builds object models by learning
the object-conditional probability distributions. The automatic
labeling of a segment is done by computing the similarity of its
feature distribution to the distribution of the learned object models
using the Kullback–Leibler divergence. The performances of the
unsupervised segmentation and object detection algorithms are
evaluated qualitatively and quantitatively using three different
data sets with comparative experiments, and the results show that
the proposed methods are able to automatically detect, group, and
label segments belonging to the same object classes
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