Search results for "Relevance feedback"
showing 7 items of 17 documents
FCA-based knowledge representation and local generalized linear models to address relevance and diversity in diverse social images
2019
Abstract 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…
IOWA Operators and Its Application to Image Retrieval
2014
This paper presents a relevance feedback procedure based on logistic regression analysis. Since, the dimension of the feature vector associated to each image is typically larger than the number of evaluated images by the user, different logistic regression models have to be fitted separately. Each fitted model provides us with a relevance probability and a confidence interval for that probability. In order to aggregate these set of probabilities and confidence intervals we use an IOWA operator. The results will show the success of our algorithm and that OWA operators are an efficient and natural way of dealing with this kind of fusion problems.
A hybrid multi-objective optimization algorithm for content based image retrieval
2013
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 retriev…
A novel dynamic multi-model relevance feedback procedure for content-based image retrieval
2016
This paper deals with the problem of image retrieval in large databases with a big semantic gap by a relevance feedback procedure. We present a novel algorithm for modelling the users's preferences in the content-based image retrieval system.The proposed algorithm considers the probability of an image belonging to the set of those sought by the user, and estimates the parameters of several local logistic regression models whose inputs are the low-level image features. A Principal Component Analysis method is applied to the original vector to reduce its high dimensionality. The relevance probabilities predicted by these local models are combined by means of a weighted average. These weights …
Distance-based relevance feedback using a hybrid interactive genetic algorithm for image retrieval
2011
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…
Improving distance based image retrieval using non-dominated sorting genetic algorithm
2015
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 relevan…
Applying logistic regression to relevance feedback in image retrieval systems
2007
This paper deals with the problem of image retrieval from large image databases. A particularly interesting problem is the retrieval of all images which are similar to one in the user's mind, taking into account his/her feedback which is expressed as positive or negative preferences for the images that the system progressively shows during the search. Here we present a novel algorithm for the incorporation of user preferences in an image retrieval system based exclusively on the visual content of the image, which is stored as a vector of low-level features. The algorithm considers the probability of an image belonging to the set of those sought by the user, and models the logit of this prob…