Learning Descriptors for Object Recognition and 3D Pose Estimation

Detecting poorly textured objects and estimating their 3D pose reliably is still a very challenging problem. We introduce a simple but powerful approach to computing descriptors for object views that efficiently capture both the object identity and 3D pose. By contrast with previous manifold-based approaches, we can rely on the Euclidean distance to evaluate the similarity between descriptors, and therefore use scalable Nearest Neighbor search methods to efficiently handle a large number of objects under a large range of poses. To achieve this, we train a Convolutional Neural Network to compute these descriptors by enforcing simple similarity and dissimilarity constraints between the descriptors. We show that our constraints nicely untangle the images from different objects and different views into clusters that are not only well-separated but also structured as the corresponding sets of poses: The Euclidean distance between descriptors is large when the descriptors are from different objects, and directly related to the distance between the poses when the descriptors are from the same object. These important properties allow us to outperform state-of-the-art object views representations on challenging RGB and RGB-D data.Comment: CVPR 201

Exponential Generalised Network Descriptors

In communication networks theory the concepts of networkness and network surplus have recently been defined. Together with transmission and betweenness centrality, they were based on the assumption of equal communication between vertices. Generalised versions of these four descriptors were presented, taking into account that communication between vertices $u$ and $v$ is decreasing as the distance between them is increasing. Therefore, we weight the quantity of communication by $\lambda^{d(u,v)}$ where $\lambda \in \left\langle0,1 \right\rangle$. Extremal values of these descriptors are analysed.Comment: 17 pages, 1 figur

Statistical Features for Image Retrieval: A Quantitative Comparison

In this paper we present a comparison between various statistical descriptors and analyze their goodness in classifying textural images. The chosen statistical descriptors have been proposed by Tamura, Battiato and Haralick. In this work we also test a combination of the three descriptors for texture analysis. The databases used in our study are the well-known Brodatz’s album and DDSM(Heath et al., 1998). The computed features are classified using the Naive Bayes, the RBF, the KNN, the Random Forest and Random Tree models. The results obtained from this study show that we can achieve a high classification accuracy if the descriptors are used all together

An accurate retrieval through R-MAC+ descriptors for landmark recognition

The landmark recognition problem is far from being solved, but with the use of features extracted from intermediate layers of Convolutional Neural Networks (CNNs), excellent results have been obtained. In this work, we propose some improvements on the creation of R-MAC descriptors in order to make the newly-proposed R-MAC+ descriptors more representative than the previous ones. However, the main contribution of this paper is a novel retrieval technique, that exploits the fine representativeness of the MAC descriptors of the database images. Using this descriptors called "db regions" during the retrieval stage, the performance is greatly improved. The proposed method is tested on different public datasets: Oxford5k, Paris6k and Holidays. It outperforms the state-of-the- art results on Holidays and reached excellent results on Oxford5k and Paris6k, overcame only by approaches based on fine-tuning strategies

Descriptors for terpene esters from chromatographic and partition measurements: Estimation of human odor detection thresholds

We have used gas chromatographic retention data together with other data to obtain Abraham descriptors for 30 terpene esters. These include the air-water partition coefficient, as log Kw, for which no experimental values are available for any terpene ester. The other descriptors are the ester dipolarity, S, the hydrogen bond basicity, B, (the ester hydrogen bond acidity is zero for the esters studied), and L the logarithm of the air-hexadecane partition coefficient. Both S and B are larger than those for simple aliphatic esters, as expected from the terpene ester structures that include ring systems and ethylenic double bonds. These descriptors can then be used to obtain a large number of physicochemical and environmental properties of terpene esters. We have analyzed experimental results on human odor detection thresholds and have constructed another equation for the calculation of these thresholds, to go with a previous equation that we have reported. Then the descriptors for terpene esters can be used to estimate the important odor detection thresholds