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Abstract

Mauricio Villegas, Roberto Paredes. A k-NN Approach for Scalable Image Annotation Using General Web Data. Big Data Meets Computer Vision: First International Workshop on Large Scale Visual Recognition and Retrieval, held in conjunction with NIPS 2012, 2012. pp. 1-5.

This paper presents a simple k-NN based image annotation method that relies only on automatically gathered Web data. It can easily change or scale the list of concepts for annotation, without requiring labeled training samples for the new concepts. In terms of MAP the performance is better than the results from the ImageCLEF 2012 Scalable Web Image Annotation Task on the same dataset. Although, in terms of F-measure they are equivalent, suggesting that a better method for choosing how many concepts to select per image is required. Large-scale issues are considered by means of linear hashing techniques. The use of dictionary definitions has been observed to be a useful resource for image annotation without manually labeled training data.