Andrés Dorado

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A generic system for automatic annotation of videos is introduced. The proposed approach is based on the premise that the rules needed to infer a set of high-level concepts from low-level descriptors cannot be defined a priori. Rather, knowledge embedded in the database and interaction with an expert user is exploited to enable system learning. Underpinning(More)
Effective ways of organizing image descriptors is a critical design step of content-based image classification systems. Suitable descriptors are selected according to the problem domain for generating the feature space. Using several descriptors improves accuracy of representation but risen some challenges such as non linear combination, expensive(More)
With the large and increasing amount of visual information available in digital libraries and the Web, efficient and robust systems for image retrieval are urgently needed. In this paper a compact color descriptor scheme and an efficient metric to compare and retrieve images is presented. An image adaptive color clustering method, called fuzzy color(More)
Content-Based Image/Video Retrieval Systems combine perceptual features such as color, texture and shape with semantic concepts for improving the quality of the query's results. In this paper, a retrieval technique combining color and texture with keywords is presented. A colorbased method and conjunction with a mining association rules technique are used(More)
The fast development of innovative tools to create user friendly and effective multimedia libraries, services and environments requires novel concepts to support storage, annotation and retrieval of huge amounts of digital audiovisual data. This article presents a technique to tackle the first instance of the problem in visual digital archives:(More)
A framework for semantic-based scene classification using relevance feedback is presented. The semantic component casts the classifier within a framework of the supervised –or learning-from-examples– paradigm. Selection of suitable examples and labeling training patterns imposes a certain burden on the user that increases with the complexity of the ontology(More)