3 research outputs found
Large scale near-duplicate image retrieval using Triples of Adjacent Ranked Features (TARF) with embedded geometric information
Most approaches to large-scale image retrieval are based on the construction
of the inverted index of local image descriptors or visual words. A search in
such an index usually results in a large number of candidates. This list of
candidates is then re-ranked with the help of a geometric verification, using a
RANSAC algorithm, for example. In this paper we propose a feature
representation, which is built as a combination of three local descriptors. It
allows one to significantly decrease the number of false matches and to shorten
the list of candidates after the initial search in the inverted index. This
combination of local descriptors is both reproducible and highly
discriminative, and thus can be efficiently used for large-scale near-duplicate
image retrieval
Cluster-wise Unsupervised Hashing for Cross-Modal Similarity Search
Large-scale cross-modal hashing similarity retrieval has attracted more and
more attention in modern search applications such as search engines and
autopilot, showing great superiority in computation and storage. However,
current unsupervised cross-modal hashing methods still have some limitations:
(1)many methods relax the discrete constraints to solve the optimization
objective which may significantly degrade the retrieval performance;(2)most
existing hashing model project heterogenous data into a common latent space,
which may always lose sight of diversity in heterogenous data;(3)transforming
real-valued data point to binary codes always results in abundant loss of
information, producing the suboptimal continuous latent space. To overcome
above problems, in this paper, a novel Cluster-wise Unsupervised Hashing (CUH)
method is proposed. Specifically, CUH jointly performs the multi-view
clustering that projects the original data points from different modalities
into its own low-dimensional latent semantic space and finds the cluster
centroid points and the common clustering indicators in its own low-dimensional
space, and learns the compact hash codes and the corresponding linear hash
functions. An discrete optimization framework is developed to learn the unified
binary codes across modalities under the guidance cluster-wise code-prototypes.
The reasonableness and effectiveness of CUH is well demonstrated by
comprehensive experiments on diverse benchmark datasets.Comment: 13 pages, 26 figure
Asymmetric Correlation Quantization Hashing for Cross-modal Retrieval
Due to the superiority in similarity computation and database storage for
large-scale multiple modalities data, cross-modal hashing methods have
attracted extensive attention in similarity retrieval across the heterogeneous
modalities. However, there are still some limitations to be further taken into
account: (1) most current CMH methods transform real-valued data points into
discrete compact binary codes under the binary constraints, limiting the
capability of representation for original data on account of abundant loss of
information and producing suboptimal hash codes; (2) the discrete binary
constraint learning model is hard to solve, where the retrieval performance may
greatly reduce by relaxing the binary constraints for large quantization error;
(3) handling the learning problem of CMH in a symmetric framework, leading to
difficult and complex optimization objective. To address above challenges, in
this paper, a novel Asymmetric Correlation Quantization Hashing (ACQH) method
is proposed. Specifically, ACQH learns the projection matrixs of heterogeneous
modalities data points for transforming query into a low-dimensional
real-valued vector in latent semantic space and constructs the stacked
compositional quantization embedding in a coarse-to-fine manner for indicating
database points by a series of learnt real-valued codeword in the codebook with
the help of pointwise label information regression simultaneously. Besides, the
unified hash codes across modalities can be directly obtained by the discrete
iterative optimization framework devised in the paper. Comprehensive
experiments on diverse three benchmark datasets have shown the effectiveness
and rationality of ACQH.Comment: 12 page