The performance of many visual recognition algorithms -e.g. face verification, person re-identification -heavily depends on the metric used to measure the similarity between input data. Recent works such as have shown how interesting it is to learn an optimal metric for a particular task using Metric Learning (ML). Most approaches learn a Mahalanobis metric based on an objective function whose constraints comes either from a set of labelled examples or, more frequently, from sets of positive (same class) and negative (different class) pairs. In the recent works that have used metric learning (ML), the works of Shen et al. [4] and Bi et al. [2] , introducing algorithms based on Boosting approach, deserves a particular attention due to the interesting properties they offers: i) they are efficient and scalable, as no semidefinite programming is required, at each iteration only the largest eigenvalue and corresponding eigenvectors are needed, ii) Like AdaBoost, they don't have any parameter to tune and is easy to implement as no sophisticated optimization techniques are involved. It hence contrasts with most of the commonly used ML methods for which hyper-parameters, often introduced for regularization purpose, have to be adjusted by cross-validation. However, one strong limitation of these approaches [2, 4] is that it requires having triplets for learning the metric i.e. constraints expressed under the form D( x c are three input vector for which the label is known: positive and negative pairs within a constraint do have to share a common vector. This is a limitation as for most of the verification task only training pairs are available (e.g. for person re-identification on the Viper dataset only pairs of same/different persons are provided for training, and thus using such triplets is not possible). One of the key contribution of this paper is to propose a metric learning approach based on boosting allowing to use pairs of points for training i.e. which can use constraints such as D(x a , x b ) < D(x c , x d ) while keeping the good properties of [4] (scalability, simplicity, no parameters, etc.). Our approach is based on the ranking algorithm RankBoost [3], known to offer 3 particularly interesting features: i) no hyperparameter, ii) great robustness to overfitting (explained with a standard VC-dimensional analysis techniques), and iii) a computational trick for reducing the complexity of the learning step. In the following, we show how to build on Rank-Boost [3] to efficiently address this metric learning problem. Moreover, we propose a method for building hierarchical face representations for efficient identity face retrieval task. The main idea is to use a ML algorithm for building a tree structured representation of a large set of faces, such that identical-identity faces belongs to the same cluster at each tree level. The hierarchical clustering is built recursively: at each level, a new metric is learned using the faces belonging to a particular tree node which is then used to locally cluster the faces and to create the sub-branches. The retrieval for a new query face is done by visiting the tree from top to bottom until a leaf is reached; if the current is a non-leaf node, the face is projected into the corresponding subspace and compared with its centroids, which allows to move to the closest child node. When a leaf is reached, the face is compared with all the faces contained in the leaf using the corresponding local metric. The search cost is then equal to the cost of traverses of the tree, plus the cost of faces comparison in the final node. This cost is much lower than of the cost to compared all the faces of the database. Our method is a extension of the method proposed by Bhattarai et al. [1] . We introduced a new method to select the ML training samples in the tree node in order to keep easily the information learned in the metric of parent nodes.
Negrel et al. REF introduced MLBoost, showing how to learn a boosted metric using pairwise constraints only, in a fast and scalable way.
Boosted Metric Learning for Efficient Identity-Based Face Retrieval
Procedings of the British Machine Vision Conference 2015
Procedings of the British Machine Vision Conference 2015
Computer Science