CLaC at ImageCLEF 2009
نویسندگان
چکیده
This paper describes our participation at ImageCLEF 2009. We participated in the photographic retrieval task (ImageCLEFPhoto). Our method is based on intermedia pseudo-relevance feedback. We have enhanced the pseudo-relevance feedback mechanism by using semantic selectional restrictions. We use Terrier for text retrieval and our own simple block-based visual retrieval engine. The results obtained at imageCLEF 2009 show that our method is robust and promising. However, there is room for improvement on the visual retrieval as well as the topics without cluster descriptions.
منابع مشابه
UAIC at ImageCLEF 2009 Photo Annotation Task
The present article describes the system used for the our first participation in the imageCLEF 2009 Photo Annotation task. For the image classification we used four components: (1) first uses face recognition, (2) second one use training data, (3) third one uses associated exif file and (4) the fourth uses default values calculated according to the degree of occurrence in the training set data....
متن کاملI2R ImageCLEF Photo Annotation 2009 Working Notes
This paper describes the method that was used for our two submission runs for the ImageCLEF Photo Annotation task.
متن کاملThe ImageCLEF Management System
The ImageCLEF image retrieval track has been part of CLEF (Cross Language Evaluation Forum) since 2003. Organizing ImageCLEF and its large participation of research groups involves a considerable amount of work and data to manage. Goal of the management system described in this paper was to create a system for the organization of ImageCLEF to reduce manual work and professionalize the structure...
متن کاملAddressing the ImageClef 2009 Challenge Using a Patch-based Visual Words Representation
This paper describes our participation at the ImageClef 2009 medical annotation task. In this task we have used the bag-of-words approach for image representation. We submitted one run, using support-vector-machines trained on the visual word histograms in multiple scales. In this task our result ranked first, with error score of 852.8.
متن کاملCLaC and CLaC-NB: Knowledge-based and corpus-based approaches to sentiment tagging
For the Affective Text task at Semeval1/Senseval-4, the CLaC team compared a knowledge-based, domain-independent approach and a standard, statistical machine learning approach to ternary sentiment annotation of news headlines. In this paper we describe the two systems submitted to the competition and evaluate their results. We show that the knowledge-based unsupervised method achieves high accu...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2009