36 research outputs found
Multiclass latent locally linear support vector machines
Kernelized Support Vector Machines (SVM) have gained the status of off-the-shelf classifiers, able to deliver state of the art performance on almost any problem. Still, their practical use is constrained by their computational and memory complexity, which grows super-linearly with the number of training samples. In order to retain the low training and testing complexity of linear classifiers and the exibility of non linear ones, a growing, promising alternative is represented by methods that learn non-linear classifiers through local combinations of linear ones. In this paper we propose a new multi class local classifier, based on a latent SVM formulation. The proposed classifier makes use of a set of linear models that are linearly combined using sample and class specific weights. Thanks to the latent formulation, the combination coefficients are modeled as latent variables. We allow soft combinations and we provide a closed-form solution for their estimation, resulting in an efficient prediction rule. This novel formulation allows to learn in a principled way the sample specific weights and the linear classifiers, in a unique optimization problem, using a CCCP optimization procedure. Extensive experiments on ten standard UCI machine learning datasets, one large binary dataset, three character and digit recognition databases, and a visual place categorization dataset show the power of the proposed approach
Selective Deep Convolutional Features for Image Retrieval
Convolutional Neural Network (CNN) is a very powerful approach to extract
discriminative local descriptors for effective image search. Recent work adopts
fine-tuned strategies to further improve the discriminative power of the
descriptors. Taking a different approach, in this paper, we propose a novel
framework to achieve competitive retrieval performance. Firstly, we propose
various masking schemes, namely SIFT-mask, SUM-mask, and MAX-mask, to select a
representative subset of local convolutional features and remove a large number
of redundant features. We demonstrate that this can effectively address the
burstiness issue and improve retrieval accuracy. Secondly, we propose to employ
recent embedding and aggregating methods to further enhance feature
discriminability. Extensive experiments demonstrate that our proposed framework
achieves state-of-the-art retrieval accuracy.Comment: Accepted to ACM MM 201