71,087 research outputs found
Embedding based on function approximation for large scale image search
The objective of this paper is to design an embedding method that maps local
features describing an image (e.g. SIFT) to a higher dimensional representation
useful for the image retrieval problem. First, motivated by the relationship
between the linear approximation of a nonlinear function in high dimensional
space and the stateof-the-art feature representation used in image retrieval,
i.e., VLAD, we propose a new approach for the approximation. The embedded
vectors resulted by the function approximation process are then aggregated to
form a single representation for image retrieval. Second, in order to make the
proposed embedding method applicable to large scale problem, we further derive
its fast version in which the embedded vectors can be efficiently computed,
i.e., in the closed-form. We compare the proposed embedding methods with the
state of the art in the context of image search under various settings: when
the images are represented by medium length vectors, short vectors, or binary
vectors. The experimental results show that the proposed embedding methods
outperform existing the state of the art on the standard public image retrieval
benchmarks.Comment: Accepted to TPAMI 2017. The implementation and precomputed features
of the proposed F-FAemb are released at the following link:
http://tinyurl.com/F-FAem
Orientation covariant aggregation of local descriptors with embeddings
Image search systems based on local descriptors typically achieve orientation
invariance by aligning the patches on their dominant orientations. Albeit
successful, this choice introduces too much invariance because it does not
guarantee that the patches are rotated consistently. This paper introduces an
aggregation strategy of local descriptors that achieves this covariance
property by jointly encoding the angle in the aggregation stage in a continuous
manner. It is combined with an efficient monomial embedding to provide a
codebook-free method to aggregate local descriptors into a single vector
representation. Our strategy is also compatible and employed with several
popular encoding methods, in particular bag-of-words, VLAD and the Fisher
vector. Our geometric-aware aggregation strategy is effective for image search,
as shown by experiments performed on standard benchmarks for image and
particular object retrieval, namely Holidays and Oxford buildings.Comment: European Conference on Computer Vision (2014
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