TIA-INAOE's Participation at ImageCLEF 2007
Hugo Jair Escalante, Jesús A. González, Carlos Hernández, Aurelio López‐López, Manuel Montes-y-Gómez, Eduardo F. Morales, Elías Ruiz, Luis Enrique Sucar, Luis Villaseñor-Pineda · 2007
This paper describes the participation of the INAOE’s research group on machine learning for image processing and information retrieval from México. This year we proposed two approaches for the photographic retrieval task. First, we studied the annotation-based expansion of documents for image retrieval. This approach consists of automatically assigning labels to images by using supervised machine learning techniques. Labels are used for expanding the manual annotations of images. Then, we build a text-based retrieval method that uses the expanded annotations. Experimental results give evidence that the expansion could be helpful for improving retrieval performance and diversifying results. However, it is not trivial to determine the best way for combining labels with the other information available. In our second formulation we adopted a late fusion approach to combine the outputs of several heterogeneous retrieval methods. Our aim was to take advantage of the diversity, complementariness and redundancy of documents through ranked lists obtained with different methods and using distinct information. We consider content-based, text-based, annotationbased,