6533b834fe1ef96bd129de8e
RESEARCH PRODUCT
Image retrieval system for citizen services using penalized logistic regression models
V. CerverónXaro BenaventE. De VesGuillermo Ayalasubject
020203 distributed computingInformation retrievalComputer scienceRelevance feedback02 engineering and technologyLogistic regressionImage (mathematics)Set (abstract data type)Smart city0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingHigh dimensionalityImage retrievalSemantic gapdescription
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.The proposed algorithm considers the probability of an image belonging to the set of those sought by the user, by selecting a set of relevant and irrelevant images to the query, and by adjusting a penalized logistic regression model to the information provided by the user. The Wikipedia2011 image collection has been used for testing purposes. The procedure has been compared to other retrieval relevance feedback procedures in recent literature. Good results have been obtained with just one global model fitted with high dimensionality data.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-11-25 | Proceedings of the 10th Euro-American Conference on Telematics and Information Systems |