103 research outputs found
KinshipGAN: Synthesizing of Kinship Faces From Family Photos by Regularizing a Deep Face Network
In this paper, we propose a kinship generator network that can synthesize a
possible child face by analyzing his/her parent's photo. For this purpose, we
focus on to handle the scarcity of kinship datasets throughout the paper by
proposing novel solutions in particular. To extract robust features, we
integrate a pre-trained face model to the kinship face generator. Moreover, the
generator network is regularized with an additional face dataset and
adversarial loss to decrease the overfitting of the limited samples. Lastly, we
adapt cycle-domain transformation to attain a more stable results. Experiments
are conducted on Families in the Wild (FIW) dataset. The experimental results
show that the contributions presented in the paper provide important
performance improvements compared to the baseline architecture and our proposed
method yields promising perceptual results.Comment: Accepted to IEEE ICIP 201
Towards Accurate Camera Geopositioning by Image Matching
In this work, we present a camera geopositioning system based on matching a
query image against a database with panoramic images. For matching, our system
uses memory vectors aggregated from global image descriptors based on
convolutional features to facilitate fast searching in the database. To speed
up searching, a clustering algorithm is used to balance geographical
positioning and computation time. We refine the obtained position from the
query image using a new outlier removal algorithm. The matching of the query
image is obtained with a recall@5 larger than 90% for panorama-to-panorama
matching. We cluster available panoramas from geographically adjacent locations
into a single compact representation and observe computational gains of
approximately 50% at the cost of only a small (approximately 3%) recall loss.
Finally, we present a coordinate estimation algorithm that reduces the median
geopositioning error by up to 20%
Class-Weighted Convolutional Features for Visual Instance Search
Image retrieval in realistic scenarios targets large dynamic datasets of
unlabeled images. In these cases, training or fine-tuning a model every time
new images are added to the database is neither efficient nor scalable.
Convolutional neural networks trained for image classification over large
datasets have been proven effective feature extractors for image retrieval. The
most successful approaches are based on encoding the activations of
convolutional layers, as they convey the image spatial information. In this
paper, we go beyond this spatial information and propose a local-aware encoding
of convolutional features based on semantic information predicted in the target
image. To this end, we obtain the most discriminative regions of an image using
Class Activation Maps (CAMs). CAMs are based on the knowledge contained in the
network and therefore, our approach, has the additional advantage of not
requiring external information. In addition, we use CAMs to generate object
proposals during an unsupervised re-ranking stage after a first fast search.
Our experiments on two public available datasets for instance retrieval,
Oxford5k and Paris6k, demonstrate the competitiveness of our approach
outperforming the current state-of-the-art when using off-the-shelf models
trained on ImageNet. The source code and model used in this paper are publicly
available at http://imatge-upc.github.io/retrieval-2017-cam/.Comment: To appear in the British Machine Vision Conference (BMVC), September
201
An accurate retrieval through R-MAC+ descriptors for landmark recognition
The landmark recognition problem is far from being solved, but with the use
of features extracted from intermediate layers of Convolutional Neural Networks
(CNNs), excellent results have been obtained. In this work, we propose some
improvements on the creation of R-MAC descriptors in order to make the
newly-proposed R-MAC+ descriptors more representative than the previous ones.
However, the main contribution of this paper is a novel retrieval technique,
that exploits the fine representativeness of the MAC descriptors of the
database images. Using this descriptors called "db regions" during the
retrieval stage, the performance is greatly improved. The proposed method is
tested on different public datasets: Oxford5k, Paris6k and Holidays. It
outperforms the state-of-the- art results on Holidays and reached excellent
results on Oxford5k and Paris6k, overcame only by approaches based on
fine-tuning strategies
Automatic Query Image Disambiguation for Content-Based Image Retrieval
Query images presented to content-based image retrieval systems often have
various different interpretations, making it difficult to identify the search
objective pursued by the user. We propose a technique for overcoming this
ambiguity, while keeping the amount of required user interaction at a minimum.
To achieve this, the neighborhood of the query image is divided into coherent
clusters from which the user may choose the relevant ones. A novel feedback
integration technique is then employed to re-rank the entire database with
regard to both the user feedback and the original query. We evaluate our
approach on the publicly available MIRFLICKR-25K dataset, where it leads to a
relative improvement of average precision by 23% over the baseline retrieval,
which does not distinguish between different image senses.Comment: VISAPP 2018 paper, 8 pages, 5 figures. Source code:
https://github.com/cvjena/ai
Deep Learning Features at Scale for Visual Place Recognition
The success of deep learning techniques in the computer vision domain has
triggered a range of initial investigations into their utility for visual place
recognition, all using generic features from networks that were trained for
other types of recognition tasks. In this paper, we train, at large scale, two
CNN architectures for the specific place recognition task and employ a
multi-scale feature encoding method to generate condition- and
viewpoint-invariant features. To enable this training to occur, we have
developed a massive Specific PlacEs Dataset (SPED) with hundreds of examples of
place appearance change at thousands of different places, as opposed to the
semantic place type datasets currently available. This new dataset enables us
to set up a training regime that interprets place recognition as a
classification problem. We comprehensively evaluate our trained networks on
several challenging benchmark place recognition datasets and demonstrate that
they achieve an average 10% increase in performance over other place
recognition algorithms and pre-trained CNNs. By analyzing the network responses
and their differences from pre-trained networks, we provide insights into what
a network learns when training for place recognition, and what these results
signify for future research in this area.Comment: 8 pages, 10 figures. Accepted by International Conference on Robotics
and Automation (ICRA) 2017. This is the submitted version. The final
published version may be slightly differen
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