ImageCLEF 2013: The vision, the data and the open challenges

This paper presents an overview of the ImageCLEF 2013 lab. Since its first edition in 2003, ImageCLEF has become one of the key initiatives promoting the benchmark evaluation of algorithms for the cross-language annotation and retrieval of images in various domains, such as public and personal images, to data acquired by mobile robot platforms and botanic collections. Over the years, by providing new data collections and challenging tasks to the community of interest, the ImageCLEF lab has achieved an unique position in the multi lingual image annotation and retrieval research landscape. The 2013 edition consisted of three tasks: the photo annotation and retrieval task, the plant identification task and the robot vision task. Furthermore, the medical annotation task, that traditionally has been under the ImageCLEF umbrella and that this year celebrates its tenth anniversary, has been organized in conjunction with AMIA for the first time. The paper describes the tasks and the 2013 competition, giving an unifying perspective of the present activities of the lab while discussion the future challenges and opportunities.This work has been partially supported by the Halser Foundation (B. C.),by the LiMoSINe FP7 project under grant # 288024 (B. T.), by the Khresmoi (grant# 257528) and PROMISE ( grant # 258191) FP 7 projects (H.M.) and by the tranScriptorium FP7 project under grant # 600707 (M. V., R. P.)Caputo ., B.; Muller ., H.; Thomee ., B.; Villegas, M.; Paredes Palacios, R.; Zellhofer ., D.; Goeau ., H.... (2013). ImageCLEF 2013: The vision, the data and the open challenges. En Information Access Evaluation. Multilinguality, Multimodality, and Visualization. Springer Verlag (Germany). 8138:250-268. https://doi.org/10.1007/978-3-642-40802-1_26S2502688138Muller, H., Clough, P., Deselaers, T., Caputo, B.: ImageCLEF: experimental evaluation in visual information retrieval. 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Preconceptualización con modelos neuronales lineal-no lineal en el sistema visual

El presente trabajo trata los procesos de normalización en el sistema visual, desde la entrada sensorial en retina, núcleo geniculado lateral (NGL) hasta el área visual V1, con el fin de encontrar un invariante de escena que sea capaz de indentificarla. Entendiendo como procesos de normalización a las transformaciones de las señales sensoriales desde la retina hasta el cortex. Los tres primeros estados con etapas lineales y no lineales, normalizan la escena ajustando la luminancia, el brillo y el contraste. El cuarto estado transforma la señal y la mapea en el espacio, en frecuencia y en ángulo, según el modelo implementado por Martinez-Garcia et al., (2018). Su correlato neuronal correspondería: los dos primeros estados con la retina, el tercero con el núcleo geniculado lateral (NGL), y el cuarto dentro de V1. A partir de este estado se propone otro nuevo, coherente con los anteriores, con tres niveles jerárquicos, que es capaz de aprender este invariante con métodos de clasificación lineal. Su correlato neuronal estaría en V1. Con este fin, se entrena un clasificador lineal con aprendizaje supervisado para el reconocimien- to de escenas, y se intenta buscar en sus parámetros el invariante de escena. Se prueba la eficiencia del nuevo estado en la predicción de escena y en el aprendizaje, tras nue- vas evidencias, que se adecúen a la nueva realidad. Se analiza en profundidad este proceso. El hecho de no poder contrastar la predicción con la realidad se ha suplido dando crédito a las predicciones. Este modelo admite aprendizaje no supervisado o semi-supervisado. A nivel visual, se comprueba la “imaginación” de los invariantes (su versión como imagen), tanto los del modelo como los que no tienen en cuenta la discriminación. Finalmente, se comparan con la entrada visual, concretamente en el estado 3. El experimento realizado es compatible con la hipótesis de que la normalización en los primeros estados es convergente, apuntando hacia la existencia de un invariante de escena