Multi-Image Semantic Matching by Mining Consistent Features

This work proposes a multi-image matching method to estimate semantic correspondences across multiple images. In contrast to the previous methods that optimize all pairwise correspondences, the proposed method identifies and matches only a sparse set of reliable features in the image collection. In this way, the proposed method is able to prune nonrepeatable features and also highly scalable to handle thousands of images. We additionally propose a low-rank constraint to ensure the geometric consistency of feature correspondences over the whole image collection. Besides the competitive performance on multi-graph matching and semantic flow benchmarks, we also demonstrate the applicability of the proposed method for reconstructing object-class models and discovering object-class landmarks from images without using any annotation.Comment: CVPR 201

Particular object retrieval with integral max-pooling of CNN activations

Recently, image representation built upon Convolutional Neural Network (CNN) has been shown to provide effective descriptors for image search, outperforming pre-CNN features as short-vector representations. Yet such models are not compatible with geometry-aware re-ranking methods and still outperformed, on some particular object retrieval benchmarks, by traditional image search systems relying on precise descriptor matching, geometric re-ranking, or query expansion. This work revisits both retrieval stages, namely initial search and re-ranking, by employing the same primitive information derived from the CNN. We build compact feature vectors that encode several image regions without the need to feed multiple inputs to the network. Furthermore, we extend integral images to handle max-pooling on convolutional layer activations, allowing us to efficiently localize matching objects. The resulting bounding box is finally used for image re-ranking. As a result, this paper significantly improves existing CNN-based recognition pipeline: We report for the first time results competing with traditional methods on the challenging Oxford5k and Paris6k datasets

Regularización de situaciones de ambigüedad en el registro de imágenes mediante modelos duales

Usually, the most critical step involved in image registration is the image matching. A common methodology when estimating the mapping that geometrically relates two images typically consists of two separate and sequential stages: initial feature matching estimation, and regularization for propagating this matching over all image areas. Parametric models representing fuzzy matching regions are proposed in order to support the initial matching. The classical approach has a main drawback, namely the detection of the common features is ambiguous when there is more than one likely matching. To alleviate this problem, the use of dual models to represent the high similarity regions is also proposed in this paper. An averaged POCS (projection onto convex sets) procedure, combined with regularization based on deformable kernels, is used to solve the multiple-choice dilemma. Implementation of these parametrization and regularization steps is described throughout the paper. The proposed approach is tested on a stereo-pair that presents multiple choices of similar likelihood, with successful results.Este trabajo ha sido financiado por el Ministerio de Ciencia y Tecnología a través del proyecto TIC2002-03033