Published in IEEE transactions on pattern analysis and machine intelligence
Textual data such as tags, sentence descriptions are combined with visual cues to reduce the semantic gap for image retrieval applications in today's Multimodal Image Retrieval (MIR) systems. However, all tags are treated as equally important in these systems, which may result in misalignment between visual and textual modalities during MIR trainin...
Published in IEEE transactions on pattern analysis and machine intelligence
Deriving from the gradient vector of a generative model of local features, Fisher vector coding (FVC) has been identified as an effective coding method for image classification. Most, if not all, FVC implementations employ the Gaussian mixture model (GMM) as the generative model for local features. However, the representative power of a GMM can be ...
Published in IEEE transactions on pattern analysis and machine intelligence
We introduce a family of Newton-type greedy selection methods for -constrained minimization problems. The basic idea is to construct a quadratic function to approximate the original objective function around the current iterate and solve the constructed quadratic program over the cardinality constraint. The next iterate is then estimated via a line...
Published in IEEE transactions on pattern analysis and machine intelligence
We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically iden...
Published in IEEE transactions on pattern analysis and machine intelligence
We propose novel finite-dimensional spaces of well-behaved transformations. The latter are obtained by (fast and highly-accurate) integration of continuous piecewise-affine velocity fields. The proposed method is simple yet highly expressive, effortlessly handles optional constraints (e.g., volume preservation and/or boundary conditions), and suppo...
Published in IEEE transactions on pattern analysis and machine intelligence
Constrained clustering uses pre-given knowledge to improve the clustering performance. Here we use a new constraint called partition level side information and propose the Partition Level Constrained Clustering (PLCC) framework' where only a small proportion of the data is given labels to guide the procedure of clustering. Our goal is to find a par...
Published in IEEE transactions on pattern analysis and machine intelligence
This paper aims for event recognition when video examples are scarce or even completely absent. The key in such a challenging setting is a semantic video representation. Rather than building the representation from individual attribute detectors and their annotations, we propose to learn the entire representation from freely available web videos an...
Published in IEEE transactions on pattern analysis and machine intelligence
In this paper, we present a method that estimates reflectance and illumination information from a single image depicting a single-material specular object from a given class under natural illumination. We follow a data-driven, learning-based approach trained on a very large dataset, but in contrast to earlier work we do not assume one or more compo...
Published in IEEE transactions on pattern analysis and machine intelligence
Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets. However, the availability of fully annotated atlas images for training is limited due to the time required for the labelling task. Segmentation methods requiring only a proportion of each atlas i...
Published in IEEE transactions on pattern analysis and machine intelligence
We study the problem of video classification for facial analysis and human action recognition. We propose a novel weakly supervised learning method that models the video as a sequence of automatically mined, discriminative sub-events (eg. onset and offset phase for "smile", running and jumping for "highjump"). The proposed model is inspired by the ...
