3 research outputs found
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
Multimodal Prediction based on Graph Representations
This paper proposes a learning model, based on rank-fusion graphs, for
general applicability in multimodal prediction tasks, such as multimodal
regression and image classification. Rank-fusion graphs encode information from
multiple descriptors and retrieval models, thus being able to capture
underlying relationships between modalities, samples, and the collection
itself. The solution is based on the encoding of multiple ranks for a query (or
test sample), defined according to different criteria, into a graph. Later, we
project the generated graph into an induced vector space, creating fusion
vectors, targeting broader generality and efficiency. A fusion vector estimator
is then built to infer whether a multimodal input object refers to a class or
not. Our method is capable of promoting a fusion model better than early-fusion
and late-fusion alternatives. Performed experiments in the context of multiple
multimodal and visual datasets, as well as several descriptors and retrieval
models, demonstrate that our learning model is highly effective for different
prediction scenarios involving visual, textual, and multimodal features,
yielding better effectiveness than state-of-the-art methods
Multimodal Prediction based on Graph Representations
This paper proposes a learning model, based on rank-fusion graphs, for general applicability in multimodal prediction tasks, such as multimodal regression and image classification. Rank-fusion graphs encode information from multiple descriptors and retrieval models, thus being able to capture underlying relationships between modalities, samples, and the collection itself. The solution is based on the encoding of multiple ranks for a query (or test sample), defined according to different criteria, into a graph. Later, we project the generated graph into an induced vector space, creating fusion vectors, targeting broader generality and efficiency. A fusion vector estimator is then built to infer whether a multimodal input object refers to a class or not. Our method is capable of promoting a fusion model better than early-fusion and late-fusion alternatives. Performed experiments in the context of multiple multimodal and visual datasets, as well as several descriptors and retrieval models, demonstrate that our learning model is highly effective for different prediction scenarios involving visual, textual, and multimodal features, yielding better effectiveness than state-of-the-art methods