This paper describes a procedure to deal with large image collections obtained by smart city services based on interaction with citizens providing pictures. The semantic gap between the low-level image features and represented concepts and situations has been addressed using image retrieval techniques. A relevance feedback procedure is proposed for Content-Based Image Retrieval (CBIR) based on the modelling of user responses. One of the novelties of the proposal is that the feedback learning procedure can use the information that citizens themselves can provide when using these services.

In social image retrieval, the main goal is to offer a relevant but also diverse result set of images to the user. To address relevance and diversity at the same time, we propose a multi-modal procedure. This approach deals with the diversification problem using a two-step procedure based on the application of Formal Concept Analysis (FCA) to organize the text content of the images, followed by a Hierarchical Agglomerative Clustering (HAC) step to find the topics addressed by the images. FCA detects the latent concepts covered by the images in the result set, organizing them according to these concepts. In the second step, clustering is carried out to group together the ones with a similar concept. To assess the relevance, we use an adaptive multi-model relevance feedback algorithm which uses the low-level visual features to estimate a relevance measurement for all the images in the dataset. Several local logistic regression models are automatically adjusted to select the best performance for each topic. Finally, the images are ranked by selecting the highest probability image at each text cluster, generating a relevant but diverse ranked list. Diverse social images 2013, 2014, 2015, 2016 and 2017 datasets from the MediaEval benchmark are used to test the performance of our approach in a real scenario. Experimental results show that the proposed joint multimedia procedure works well on multi-concept and complex collections without adjusting parameters among collections, achieving the best results with a user-based relevance feedback algorithm. Our challenge is to achieve similar results with our automatic version.

Main goal of this work is to show the improvement of using a textual pre-filtering combined with an image re-ranking in a Multimedia Information Retrieval task. The defined three step-based retrieval processes and a well-selected combination of visual and textual techniques help the developed Multimedia Information Retrieval System to overcome the semantic gap in a given query. In the paper, five different late semantic fusion approaches are discussed and experimented in a realistic scenario for multimedia retrieval like the one provided by the publicly available ImageCLEF Wikipedia Collection.

This paper presents a texture descriptor based on the fine detail coefficients at three resolution levels of a traslation invariant undecimated wavelet transform. First, we consider vertical and horizontal wavelet detail coefficients at the same position as the components of a bivariate random vector, and the magnitude and angle of these vectors are computed. The magnitudes are modeled by a Generalized Gamma distribution. Their parameters, together with the circular histograms of angles, are used to characterize each texture image of the database. The Kullback-Leibler divergence is used as the similarity measurement. Retrieval experiments, in which we compare two wavelet transforms, are carried out on the Brodatz texture collection. Results reveal the good performance of this wavelet-based texture descriptor obtained via the Generalized Gamma distribution.

This paper describes Intelligent Eye, a mobile phone interactive leisure guide that offers location-based multimedia information. The information offered is related to the user's position, so the main goal of this work is the development of an efficient system to detect where the user is pointing his/her camera at by means of a content-based image retrieval algorithm (CBIR). The CBIR procedure uses color histograms in the HS color space extracted from images, and employs Kullback-Leibler divergence as the similarity measure. Intelligent Eye can be used in a wide range of camera-equipped mobile phones; however, efficiency is improved if GPS data is available. In order to outperform other systems we have made use of a video stream in the classification process instead of using still images. Moreover, the image database can be populated dynamically by means of a feedback procedure with images taken by users. We report preliminary results of the prototype working with real images obtaining a hit classification rate of 96%.

Fovea segmentation in fluorescein angiographies is a fundamental first task in any study of ocular diseases. The importance of fovea detection is due to the fact that the nearer the centre of the fovea a lesion is, the graver this lesion is. The proposed method is based on B-snakes and uses a greedy algorithm to minimise an appropriate energy which accurately leads to a convenient characterisation of the boundary of the foveal zone. The first initialisation step, which consists of finding the most appropriate local minimum along with a procedure to construct an initial contour involving a region growing algorithm, leads to a convenient and robust initialisation of the proposed active contour model.

Describes an algorithm for registering retinal angiograms. The idea is to exploit the network structure of the blood vessel patterns to improve the recovery of the affine registration parameters. The framework for our study is provided by the generalised EM algorithm. There are two novel features of the algorithm. Firstly, in the expectation step of the algorithm we update the probabilities of correspondence matching using contextual constraints provided by the network structure of the blood-vessel pattern in the angiograms. The second novel idea is to recover affine transformation parameters by applying the Gustafson fuzzy clustering procedure to the expected log-likelihood function.