14 research outputs found
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
Efficient Nearest Neighbors Search for Large-Scale Landmark Recognition
The problem of landmark recognition has achieved excellent results in
small-scale datasets. When dealing with large-scale retrieval, issues that were
irrelevant with small amount of data, quickly become fundamental for an
efficient retrieval phase. In particular, computational time needs to be kept
as low as possible, whilst the retrieval accuracy has to be preserved as much
as possible. In this paper we propose a novel multi-index hashing method called
Bag of Indexes (BoI) for Approximate Nearest Neighbors (ANN) search. It allows
to drastically reduce the query time and outperforms the accuracy results
compared to the state-of-the-art methods for large-scale landmark recognition.
It has been demonstrated that this family of algorithms can be applied on
different embedding techniques like VLAD and R-MAC obtaining excellent results
in very short times on different public datasets: Holidays+Flickr1M, Oxford105k
and Paris106k
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-theart
results on Holidays and reached excellent results on Oxford5k and Paris6k, overcame only by approaches based on fine-tuning strategies
Diffusion Parameters Analysis in a Content-Based Image Retrieval Task for Mobile Vision
Most recent computer vision tasks take into account the distribution of image features to obtain more powerful models and better performance. One of the most commonly used techniques to this purpose is the diffusion algorithm, which fuses manifold data and k-Nearest Neighbors (kNN) graphs. In this paper, we describe how we optimized diffusion in an image retrieval task aimed at mobile vision applications, in order to obtain a good trade-off between computation load and performance. From a computational efficiency viewpoint, the high complexity of the exhaustive creation of a full kNN graph for a large database renders such a process unfeasible on mobile devices. From a retrieval performance viewpoint, the diffusion parameters are strongly task-dependent and affect significantly the algorithm performance. In the method we describe herein, we tackle the first issue by using approximate algorithms in building the kNN tree. The main contribution of this work is the optimization of diffusion parameters using a genetic algorithm (GA), which allows us to guarantee high retrieval performance in spite of such a simplification. The results we have obtained confirm that the global search for the optimal diffusion parameters performed by a genetic algorithm is equivalent to a massive analysis of the diffusion parameter space for which an exhaustive search would be totally unfeasible. We show that even a grid search could often be less efficient (and effective) than the GA, i.e., that the genetic algorithm most often produces better diffusion settings when equal computing resources are available to the two approaches. Our method has been tested on several publicly-available datasets: Oxford5k, ROxford5k, Paris6k, RParis6k, and Oxford105k, and compared to other mainstream approaches
An Efficient Approximate kNN Graph Method for Diffusion on Image Retrieval
The application of the diffusion in many computer vision and artificial intelligence projects has been shown to give excellent improvements in performance. One of the main bottlenecks of this technique is the quadratic growth of the kNN graph size due to the high-quantity of new connections between nodes in the graph, resulting in long computation times. Several strategies have been proposed to address this, but none are effective and efficient. Our novel technique, based on LSH projections, obtains the same performance as the exact kNN graph after diffusion, but in less time (approximately 18 times faster on a dataset of a hundred thousand images). The proposed method was validated and compared with other state-of-the-art on several public image datasets, including Oxford5k, Paris6k, and Oxford105k
Genetic Algorithms for the Optimization of Diffusion Parameters in Content-Based Image Retrieval
Several computer vision and artificial intelligence projects are nowadays exploiting the manifold data distribution using, e.g., the diffusion process. This approach has produced dramatic improvements on the final performance thanks to the application of such algorithms to the kNN graph. Unfortunately, this recent technique needs a manual configuration of several parameters, thus it is not straightforward to find the best configuration for each dataset. Moreover, the brute-force approach is computationally very demanding when used to optimally set the parameters of the diffusion approach.
We propose to use genetic algorithms to find the optimal setting of all the diffusion parameters with respect to retrieval performance for each different dataset. Our approach is faster than others used as references (brute-force, random-search and PSO). A comparison with these methods has been made on three public image datasets: Oxford5k, Paris6k and Oxford105k