Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
International Journal of Computer Vision
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
Descriptive visual words and visual phrases for image applications
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Building contextual visual vocabulary for large-scale image applications
Proceedings of the international conference on Multimedia
Contour Detection and Hierarchical Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This dissertation research will explore region-based and interest points based image representations, two of the most-used image models for object detection, image classification, and visual search among other applications. We will analyze the relationship between both representations with the goal of proposing a new hybrid representation that takes advantage of the strengths and overcomes the weaknesses of both approaches. More specifically, we will focus on the gPb-owt-ucm segmentation algorithm and the SIFT local features since they are the most contrasted techniques in their respective fields. Furthermore, using an object retrieval benchmark, this dissertation research will analyze three basic questions: (i) the usefulness of an interest points hierarchy based on a contour strength signal, (ii) the influence of the context on both interest points location and description, and (iii) the analysis of regions as spatial support for bundling interest points.