Image Retrieval and Annotation Using Maximum Entropy [chapter]

Thomas Deselaers, Tobias Weyand, Hermann Ney
<span title="">2007</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
We present and discuss our participation in the four tasks of the ImageCLEF 2006 Evaluation. In particular, we present a novel approach to learn feature weights in our content-based image retrieval system FIRE. Given a set of training images with known relevance among each other, the retrieval task is reformulated as a classification task and then the weights to combine a set of features are trained discriminatively using the maximum entropy framework. Experimental results for the medical
more &raquo; ... val task show large improvements over heuristically chosen weights. Furthermore the maximum entropy approach is used for the automatic image annotation tasks in combination with a part-based object model. Using our object classification methods, we obtained the best results in the medical and in the object annotation task. Retrieval Tasks ImageCLEF 2006 hosted two independent retrieval tasks: The medical retrieval task [6] and the photo retrieval task [7] .
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-540-74999-8_91">doi:10.1007/978-3-540-74999-8_91</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4hk4kdhygnhhrl74hqqtgsmawi">fatcat:4hk4kdhygnhhrl74hqqtgsmawi</a> </span>
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