Salvador Moreno-Picot

Distance-based relevance feedback using a hybrid interactive genetic algorithm for image retrieval

Content-based image retrieval (CBIR) systems aim to provide a means to find pictures in large repositories without using any other information except the own content of the images, which is usually represented as a feature vector extracted from low-level descriptors. This paper describes a CBIR algorithm which combines relevance feedback, evolutionary computation concepts and distance-based learning in an attempt to reduce the existing gap between the high level semantic content of the images and the information provided by their low-level descriptors. In particular, a framework which is independent from the particular features used is presented. The effect of different crossover strategies and mutation rates is evaluated, and the performance of the technique is compared to that of other existing algorithms, obtaining considerably better and very promising results.

A hybrid multi-objective optimization algorithm for content based image retrieval

Abstract Relevance feedback methods in CBIR (Content Based Image Retrieval) iteratively use relevance information from the user to search the space for other relevant samples. As several regions of interest may be scattered through the space, an effective search algorithm should balance the exploration of the space to find new potential regions of interest and the exploitation of areas around samples which are known relevant. However, many algorithms concentrate the search on areas which are close to the images that the user has marked as relevant, according to a distance function in the (possibly deformed) multidimensional feature space. This maximizes the number of relevant images retrieved at the first iterations, but limits the discovery of new regions of interest and may leave unexplored a large section of the space. In this paper, we propose a novel hybrid approach that uses a scattered search algorithm based on NSGA II (Non-dominated Sorting Genetic Algorithm) only at the first iteration of the relevance feedback process, and then switches to an exploitation algorithm. The combined approach has been tested on three databases and in combination with several other methods. When the hybrid method does not produce better results from the first iteration, it soon catches up and improves both precision and recall.

A Linear Cost Algorithm to Compute the Discrete Gabor Transform

In this paper, we propose an alternative efficient method to calculate the Gabor coefficients of a signal given a synthesis window with a support of size much lesser than the length of the signal. The algorithm uses the canonical dual of the window (which does not need to be calculated beforehand) and achieves a computational cost that is linear with the signal length in both analysis and synthesis. This is done by exploiting the block structure of the matrices and using an ad hoc Cholesky decomposition of the Gabor frame matrix.

Improving distance based image retrieval using non-dominated sorting genetic algorithm

Image retrieval is formulated as a multiobjective optimization problem.A multiobjective genetic algorithm is hybridized with distance based search.A parameter balances exploration (genetic search) or exploitation (nearest neighbors).Extensive comparative experimentation illustrate and assess the proposed methodology. Relevance feedback has been adopted as a standard in Content Based Image Retrieval (CBIR). One major difficulty that algorithms have to face is to achieve and adequate balance between the exploitation of already known areas of interest and the exploration of the feature space to find other relevant areas. In this paper, we evaluate different ways to combine two existing relevance feedback methods that place unequal emphasis on exploration and exploitation, in the context of distance-based methods. The hybrid approach proposed has been evaluated by using three image databases of various sizes that use different descriptors. Results show that the hybrid technique performs better than any of the original methods, highlighting the benefits of combining exploitation and exploration in relevance feedback tasks.

Searching for hidden sector in multiparticle production at LHC

We study the impact of a hidden sector beyond the Standard Model, e.g. a Hidden Valley model, on factorial moments and cumulants of multiplicity distributions in multiparticle production with a special emphasis on the prospects for LHC results.

A NSGA Based Approach for Content Based Image Retrieval

The purpose of CBIR Content Based Image Retrieval systems is to allow users to retrieve pictures related to a semantic concept of their interest, when no other information but the images themselves is available. Commonly, a series of images are presented to the user, who judges on their relevance. Several different models have been proposed to help the construction of interactive systems based on relevance feedback. Some of these models consider that an optimal query point exists, and focus on adapting the similarity measure and moving the query point so that it appears close to the relevant results and far from those which are non-relevant. This implies a strong causality between the low level features and the semantic content of the images, an assumption which does not hold true in most cases. In this paper, we propose a novel method that considers the search as a multi-objective optimization problem. Each objective consists of minimizing the distance to one of the images the user has considered relevant. Representatives of the Pareto set are considered as points of interest in the search space, and parallel searches are performed for each point of interest. Results are then combined and presented to the user. A comparatively good performance has been obtained when evaluated against other baseline methods.