User Tags Training Set Conditional Probability Estimation Conditional Probability Distribution Tag

Inner Mongolia University have participated the Visual concept detection, annotation, and retrieval using Flickr photos task of ImageCLEF for the first time in 2012. We have conducted experiments and submitted results for both the Concept Annotation and the Conceptbased Retrieval subtasks. This paper describes the methods we have adopted and the analysis of the results for the two subtasks. We focus our attention mainly on the user’s tag since we believe that user annotation provides strong semantic information which can be used to accurately determine the presence or absence of each concept and the relevance level between the images and queries. For the Concept Annotation subtask, we use only a simple statistical method that scores the confidence of the presence of each concept by the maximum conditional probability of the concept between the different given tags. For the Concept-based Retrieval task, we adopted the language modeling approach which has been widely used in text information retrieval field. Official evaluations show that the performance of our method is competitive. We rank in the middle of the pack for the Concept Annotation subtask with the best run’s MiAP equal 0.2441. For the Concept-based Retrieval subtask, we rank at the top with the best run’s MnAP equal 0.0933. Beside the main submissions, we also submit two visual runs, although no very good, with the MiAP for Concept Annotation is 0.0819 and the MnAP for Concept Retrieval is 0.0045. As a whole, the results confirm that although the methods we have adopted are simple, the performances we have achieved are satisfied.