Affine invariant visual phrases for object instance recognition

Object instance recognition approaches based on the bag-of-words model are severely affected by the loss of spatial consistency during retrieval. As a result, costly RANSAC verification is needed to ensure geometric consistency between the query and the retrieved images. A common alternative is to inject geometric informa- tion directly into the retrieval procedure, by endowing the visual words with additional information. Most of the existing approaches in this category can efficiently handle only restricted classes of geometric transfor- mations, including scale and translation. In this pa- per, we propose a simple and efficient scheme that can cover the more complex class of full affine transforma- tions. We demonstrate the usefulness of our approach in the case of planar object instance recognition, such as recognition of books, logos, traffic signs, etc.This work was funded by a Google Faculty Research Award, the Marie Curie grant CIG-334283-HRGP, a CNRS chaire d'excellence.This is the author accepted manuscript. The final version is available at http://dx.doi.org/10.1109/MVA.2015.715312

Multi-Layer Local Graph Words for Object Recognition

In this paper, we propose a new multi-layer structural approach for the task of object based image retrieval. In our work we tackle the problem of structural organization of local features. The structural features we propose are nested multi-layered local graphs built upon sets of SURF feature points with Delaunay triangulation. A Bag-of-Visual-Words (BoVW) framework is applied on these graphs, giving birth to a Bag-of-Graph-Words representation. The multi-layer nature of the descriptors consists in scaling from trivial Delaunay graphs - isolated feature points - by increasing the number of nodes layer by layer up to graphs with maximal number of nodes. For each layer of graphs its own visual dictionary is built. The experiments conducted on the SIVAL and Caltech-101 data sets reveal that the graph features at different layers exhibit complementary performances on the same content and perform better than baseline BoVW approach. The combination of all existing layers, yields significant improvement of the object recognition performance compared to single level approaches.Comment: International Conference on MultiMedia Modeling, Klagenfurt : Autriche (2012

Siamese Instance Search for Tracking

In this paper we present a tracker, which is radically different from state-of-the-art trackers: we apply no model updating, no occlusion detection, no combination of trackers, no geometric matching, and still deliver state-of-the-art tracking performance, as demonstrated on the popular online tracking benchmark (OTB) and six very challenging YouTube videos. The presented tracker simply matches the initial patch of the target in the first frame with candidates in a new frame and returns the most similar patch by a learned matching function. The strength of the matching function comes from being extensively trained generically, i.e., without any data of the target, using a Siamese deep neural network, which we design for tracking. Once learned, the matching function is used as is, without any adapting, to track previously unseen targets. It turns out that the learned matching function is so powerful that a simple tracker built upon it, coined Siamese INstance search Tracker, SINT, which only uses the original observation of the target from the first frame, suffices to reach state-of-the-art performance. Further, we show the proposed tracker even allows for target re-identification after the target was absent for a complete video shot.Comment: This paper is accepted to the IEEE Conference on Computer Vision and Pattern Recognition, 201

A Brief Review On Image Retrieval Techniques and its Scope

This paper presents the novel approach for image retrieval. Image retrieval is an important problem in many applications, such as copyright infringement detection, tag annotation, commercial retrieval, and landmark identification. Image retrieval definition is given and the concept and significance of image retrieval is also provided. Various image retrieval techniques based on content based, sketch based, also based on image annotation is explained here. The last section includes the approach for retrieval is given as a problem formulation