3,133 research outputs found
Local Descriptors Optimized for Average Precision
Extraction of local feature descriptors is a vital stage in the solution
pipelines for numerous computer vision tasks. Learning-based approaches improve
performance in certain tasks, but still cannot replace handcrafted features in
general. In this paper, we improve the learning of local feature descriptors by
optimizing the performance of descriptor matching, which is a common stage that
follows descriptor extraction in local feature based pipelines, and can be
formulated as nearest neighbor retrieval. Specifically, we directly optimize a
ranking-based retrieval performance metric, Average Precision, using deep
neural networks. This general-purpose solution can also be viewed as a listwise
learning to rank approach, which is advantageous compared to recent local
ranking approaches. On standard benchmarks, descriptors learned with our
formulation achieve state-of-the-art results in patch verification, patch
retrieval, and image matching.Comment: 13 pages, 8 figures. IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 201
Unsupervised Graph-based Rank Aggregation for Improved Retrieval
This paper presents a robust and comprehensive graph-based rank aggregation
approach, used to combine results of isolated ranker models in retrieval tasks.
The method follows an unsupervised scheme, which is independent of how the
isolated ranks are formulated. Our approach is able to combine arbitrary
models, defined in terms of different ranking criteria, such as those based on
textual, image or hybrid content representations.
We reformulate the ad-hoc retrieval problem as a document retrieval based on
fusion graphs, which we propose as a new unified representation model capable
of merging multiple ranks and expressing inter-relationships of retrieval
results automatically. By doing so, we claim that the retrieval system can
benefit from learning the manifold structure of datasets, thus leading to more
effective results. Another contribution is that our graph-based aggregation
formulation, unlike existing approaches, allows for encapsulating contextual
information encoded from multiple ranks, which can be directly used for
ranking, without further computations and post-processing steps over the
graphs. Based on the graphs, a novel similarity retrieval score is formulated
using an efficient computation of minimum common subgraphs. Finally, another
benefit over existing approaches is the absence of hyperparameters.
A comprehensive experimental evaluation was conducted considering diverse
well-known public datasets, composed of textual, image, and multimodal
documents. Performed experiments demonstrate that our method reaches top
performance, yielding better effectiveness scores than state-of-the-art
baseline methods and promoting large gains over the rankers being fused, thus
demonstrating the successful capability of the proposal in representing queries
based on a unified graph-based model of rank fusions
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