Information retrieval in multimedia databases using relevance feedback algorithms. Applying logistic regression to relevance feedback in image retrieval systems

This master tesis 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, a novel algorithm is presented 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 probability as the output of a linear model whose inputs are the low level image features. The image database is ranked by the output of the model and shown to the user, who selects a few positive and negative samples, repeating the process in an iterative way until he/she is satisfied. The problem of the small sample size with respect to the number of features is solved by adjusting several partial linear models and combining their relevance probabilities by means of an ordered weighted averaged (OWA) operator. Experiments were made with 40 users and they exhibited good performance in finding a target image (4 iterations on average) in a database of about 4700 imagesZuccarello, PD. (2007). Information retrieval in multimedia databases using relevance feedback algorithms. Applying logistic regression to relevance feedback in image retrieval systems. http://hdl.handle.net/10251/12196Archivo delegad

AUTOMATED STAR/GALAXY DISCRIMINATION IN MULTISPECTRAL WIDE-FIELD IMAGES

Abstract: In this paper we present an automated method for classifying astronomical objects in multi-spectral widefield images. The classification method is divided into three main stages. The first one consists of locating and matching the astronomical objects in the multi-spectral images. In the second stage we create a compact representation of each object applying principal component analysis to the images. In the last stage we classify the astronomical objects using locally weighted linear regression and a novel oversampling algorithm to deal with the unbalance that is inherent to this class of problems. Our experimental results show that our method performs accurate classification using small training sets and in the presence of significant class unbalance