1,356 research outputs found
Packing and Padding: Coupled Multi-index for Accurate Image Retrieval
In Bag-of-Words (BoW) based image retrieval, the SIFT visual word has a low
discriminative power, so false positive matches occur prevalently. Apart from
the information loss during quantization, another cause is that the SIFT
feature only describes the local gradient distribution. To address this
problem, this paper proposes a coupled Multi-Index (c-MI) framework to perform
feature fusion at indexing level. Basically, complementary features are coupled
into a multi-dimensional inverted index. Each dimension of c-MI corresponds to
one kind of feature, and the retrieval process votes for images similar in both
SIFT and other feature spaces. Specifically, we exploit the fusion of local
color feature into c-MI. While the precision of visual match is greatly
enhanced, we adopt Multiple Assignment to improve recall. The joint cooperation
of SIFT and color features significantly reduces the impact of false positive
matches.
Extensive experiments on several benchmark datasets demonstrate that c-MI
improves the retrieval accuracy significantly, while consuming only half of the
query time compared to the baseline. Importantly, we show that c-MI is well
complementary to many prior techniques. Assembling these methods, we have
obtained an mAP of 85.8% and N-S score of 3.85 on Holidays and Ukbench
datasets, respectively, which compare favorably with the state-of-the-arts.Comment: 8 pages, 7 figures, 6 tables. Accepted to CVPR 201
HPatches: A benchmark and evaluation of handcrafted and learned local descriptors
In this paper, we propose a novel benchmark for evaluating local image
descriptors. We demonstrate that the existing datasets and evaluation protocols
do not specify unambiguously all aspects of evaluation, leading to ambiguities
and inconsistencies in results reported in the literature. Furthermore, these
datasets are nearly saturated due to the recent improvements in local
descriptors obtained by learning them from large annotated datasets. Therefore,
we introduce a new large dataset suitable for training and testing modern
descriptors, together with strictly defined evaluation protocols in several
tasks such as matching, retrieval and classification. This allows for more
realistic, and thus more reliable comparisons in different application
scenarios. We evaluate the performance of several state-of-the-art descriptors
and analyse their properties. We show that a simple normalisation of
traditional hand-crafted descriptors can boost their performance to the level
of deep learning based descriptors within a realistic benchmarks evaluation
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