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In this paper, we propose an algorithm for learning a general class of similarity measures for kNN classification. This class encompasses, among others, the standard cosine measure, as well as the Dice and Jaccard coefficients. The algorithm we propose is an extension of the voted perceptron algorithm and allows one to learn different types of similarity functions (either based on diagonal, symmetric...
The task of ad hoc photographic image retrieval in ImageCLEF 2007 international benchmark is to retrieve relevant images in the database to the user query formulated as keywords and image examples. This paper presents rich representation and indexing technologies exploited in our system that participated in ImageCLEF 2007. It uses diverse visual content representation, text representation, pseudo-relevance...
The classification image into one of several categories is a problem arisen naturally under a wide range of circumstances. In this paper, we present a novel unsupervised model for the image classification based on featurepsilas distribution of particular patches of images. Our method firstly divides an image into grids and then constructs a hierarchical tree in order to mine the feature information...
In this paper, we present a knowledge-assisted approach to index and retrieve large volume of medical images. Both images and associated texts are indexed using medical concepts from the Unified Medical Language System (UMLS) meta-thesaurus. We propose a structured learning framework for modular acquisition of medical semantics from images with complementary global and local image indexing schemes...
Camera phones present new opportunities and challenges for mobile information association and retrieval. The visual input in the real environment is a new and rich interaction modality between a mobile user and vast information base connected to a user's device via rapidly advancing communication infrastructure. We have developed a system for tourist information access to provide scene description...
UMLS is known as largest thesaurus in biomedical domain constructed by Library National of Medicine. In this paper, we aim to evaluate effect of the exploration of UMLS knowledge in medical domain information retrieval by mapping large text of collection ImageCLEFMed to UMLS concepts, and expanding queries and documents automatically base on semantic relations in the UMLS hierarchy. We get the encouraging...
Voluminous medical images are generated daily. They are critical assets for medical diagnosis, research, and teaching. To facilitate automatic indexing and retrieval of large medical-image databases, both images and associated texts are indexed using medical concepts from the Unified Medical Language System (UMLS) meta-thesaurus. We propose a structured learning framework based on support vector machines...
Weblog (usually shortened as blog) has gained its popularity lately. There are about 70,000 new blogs a day and about 29,100 blog updates an hour. As an emerging blogging phenomenon, with the proliferation of camera phones, mobile bloggers can write their blogs almost instantaneously. But how much further can current mobile blogging tools enhance the experience? In this paper, we propose a Mobilog...
In order to solve medical multimodal queries, we propose to split the queries in different dimensions using ontology. We extract both textual and visual terms depending on the ontology dimension they belong to. Based on these terms, we build different sub queries each corresponds to one query dimension. Then we use Boolean expressions on these sub queries to filter the entire document collection....
With the proliferation of camera phones, new information retrieval applications will emerge. The image of a scene captured by a camera phone can be a query to a remote server to identify the scene and return relevant information. But unconstrained scene identification is an open problem. In this paper, we propose a discriminative measure to rank image patterns sampled from target scene classes. Support...
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