A comparative study of local descriptors for object category recognition: SIFT vs HMAX
Pattern Recognition and Image Analysis: Third Iberian Conference, IbPRIA 2007 …, 2007•Springer
In this paper we evaluate the performance of the two most successful state-of-the-art
descriptors, applied to the task of visual object detection and localization in images. In the
first experiment we use these descriptors, combined with binary classifiers, to test the
presence/absence of object in a target image. In the second experiment, we try to locate
faces in images, by using a structural model. The results show that HMAX performs slightly
better than SIFT in these tasks.
descriptors, applied to the task of visual object detection and localization in images. In the
first experiment we use these descriptors, combined with binary classifiers, to test the
presence/absence of object in a target image. In the second experiment, we try to locate
faces in images, by using a structural model. The results show that HMAX performs slightly
better than SIFT in these tasks.
Abstract
In this paper we evaluate the performance of the two most successful state-of-the-art descriptors, applied to the task of visual object detection and localization in images. In the first experiment we use these descriptors, combined with binary classifiers, to test the presence/absence of object in a target image. In the second experiment, we try to locate faces in images, by using a structural model. The results show that HMAX performs slightly better than SIFT in these tasks.
Springer
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