Search results for "Semantic gap"
showing 8 items of 8 documents
High Locality Representations for Automated Programming
2011
We study the locality of the genotype-phenotype mapping used in grammatical evolution (GE). GE is a variant of genetic programming that can evolve complete programs in an arbitrary language using a variable-length binary string. In contrast to standard GP, which applies search operators directly to phenotypes, GE uses an additional mapping and applies search operators to binary genotypes. Therefore, there is a large semantic gap between genotypes (binary strings) and phenotypes (programs or expressions). The case study shows that the mapping used in GE has low locality leading to low performance of standard mutation operators. The study at hand is an example of how basic design principles o…
La iconografía en la era digital: hacia una heurística para el estudio del contenido de las imágenes medievales
2014
Este artículo invita a reflexionar sobre la validez de la iconografía como método para describir los temas representados en las obras artísticas medievales. En esta ocasión, se ha puesto un mayor énfasis en investigar sus implicaciones epistemológicas y los sesgos que resultan del proceso de transformar las imágenes en palabras. El objetivo principal es tratar de que aflore la llamada 'brecha semántica', una especie de barrera que impide representar verbalmente un medio no léxico, como es el visual, de manera satisfactoria y sin mermas. Tras un sucinto recorrido por el pensamiento griego, con un especial interés por la écfrasis, se sugiere que el aparente equilibrio entre las capacidades se…
Learning to Rank Images for Complex Queries in Concept-based Search
2018
Concept-based image search is an emerging search paradigm that utilizes a set of concepts as intermediate semantic descriptors of images to bridge the semantic gap. Typically, a user query is rather complex and cannot be well described using a single concept. However, it is less effective to tackle such complex queries by simply aggregating the individual search results for the constituent concepts. In this paper, we propose to introduce the learning to rank techniques to concept-based image search for complex queries. With freely available social tagged images, we first build concept detectors by jointly leveraging the heterogeneous visual features. Then, to formulate the image relevance, …
Export of Relational Databases to RDF Databases by Model Transformations
2011
The Semantic Web is a Web of Data. To fulfill this web with data we need methods how to transfer business data from existing relational databases. In most cases, textual mapping languages are used for the specification of correspondences between relational DB schema and OWL ontology. These languages generally are rather awkward and not well-suited for the specification of mappings in cases when there is a substantial semantic gap between the source ER schema and the target OWL ontology. At the same time specification of mappings is a classical use case for graphical model transformation languages. In our previous work [10] we have proposed a new, model transformation-based method for the sp…
Image retrieval system for citizen services using penalized logistic regression models
2020
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 …
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 …
Modeling user preferences in content-based image retrieval: A novel attempt to bridge the semantic gap
2015
This paper is concerned with content-based image retrieval from a stochastic point of view. The semantic gap problem is addressed in two ways. First, a dimensional reduction is applied using the (pre-calculated) distances among images. The dimension of the reduced vector is the number of preferences that we allow the user to choose from, in this case, three levels. Second, the conditional probability distribution of the random user preference, given this reduced feature vector, is modeled using a proportional odds model. A new model is fitted at each iteration. The score used to rank the image database is based on the estimated probability function of the random preference. Additionally, so…
WiseNET - smart camera network interacting with a semantic model
2016
This paper presents an innovative concept for a distributed system that combines a smart camera network with semantic reasoning. The proposed system is context sensitive and combines the information extracted by the smart camera with logic rules and knowledge of what the camera observes, building information and events that may occurred. The proposed system is a justification for the use of smart cameras, and it can improve the classical visual sensor networks (VSN) and enhance the standard computer vision approach. The main application of our system is smart building management, where we specifically focus on increasing the services of the building users.