2,340 research outputs found
Deep Multi-view Learning to Rank
We study the problem of learning to rank from multiple information sources.
Though multi-view learning and learning to rank have been studied extensively
leading to a wide range of applications, multi-view learning to rank as a
synergy of both topics has received little attention. The aim of the paper is
to propose a composite ranking method while keeping a close correlation with
the individual rankings simultaneously. We present a generic framework for
multi-view subspace learning to rank (MvSL2R), and two novel solutions are
introduced under the framework. The first solution captures information of
feature mappings from within each view as well as across views using
autoencoder-like networks. Novel feature embedding methods are formulated in
the optimization of multi-view unsupervised and discriminant autoencoders.
Moreover, we introduce an end-to-end solution to learning towards both the
joint ranking objective and the individual rankings. The proposed solution
enhances the joint ranking with minimum view-specific ranking loss, so that it
can achieve the maximum global view agreements in a single optimization
process. The proposed method is evaluated on three different ranking problems,
i.e. university ranking, multi-view lingual text ranking and image data
ranking, providing superior results compared to related methods.Comment: Published at IEEE TKD
A review of multi-instance learning assumptions
Multi-instance (MI) learning is a variant of inductive machine learning, where each learning example contains a bag of instances instead of a single feature vector. The term commonly refers to the supervised setting, where each bag is associated with a label. This type of representation is a natural fit for a number of real-world learning scenarios, including drug activity prediction and image classification, hence many MI learning algorithms have been proposed. Any MI learning method must relate instances to bag-level class labels, but many types of relationships between instances and class labels are possible. Although all early work in MI learning assumes a specific MI concept class known to be appropriate for a drug activity prediction domain; this âstandard MI assumptionâ is not guaranteed to hold in other domains. Much of the recent work in MI learning has concentrated on a relaxed view of the MI problem, where the standard MI assumption is dropped, and alternative assumptions are considered instead. However, often it is not clearly stated what particular assumption is used and how it relates to other assumptions that have been proposed. In this paper, we aim to clarify the use of alternative MI assumptions by reviewing the work done in this area
Deep Fishing: Gradient Features from Deep Nets
Convolutional Networks (ConvNets) have recently improved image recognition
performance thanks to end-to-end learning of deep feed-forward models from raw
pixels. Deep learning is a marked departure from the previous state of the art,
the Fisher Vector (FV), which relied on gradient-based encoding of local
hand-crafted features. In this paper, we discuss a novel connection between
these two approaches. First, we show that one can derive gradient
representations from ConvNets in a similar fashion to the FV. Second, we show
that this gradient representation actually corresponds to a structured matrix
that allows for efficient similarity computation. We experimentally study the
benefits of transferring this representation over the outputs of ConvNet
layers, and find consistent improvements on the Pascal VOC 2007 and 2012
datasets.Comment: To appear at BMVC 201
A Hybrid Real-Time Vision-Based Person Detection Method
[EN] In this paper, we introduce a hybrid real-time
method for vision-based pedestrian detection
made up by the sequential combination of two
basic methods applied in a coarse to fine fashion.
The proposed method aims to achieve an
improved balance between detection accuracy
and computational load by taking advantage
of the strengths of these basic techniques.
Haar-like features combined with Boosting
techniques, which have been demonstrated to
provide rapid but not accurate enough results in
human detection, are used in the first stage to
provide a preliminary candidate selection in the
scene. Then, feature extraction and classification
methods, which present high accuracy rates at
expenses of a higher computational cost, are
applied over boosting candidates providing the
final prediction. Experimental results show that
the proposed method performs effectively and
efficiently, which supports its suitability for real
applications.This work is supported by CASBLIP project 6-th FP\cite{RefCASBLIP}. The authors acknowledge the support of the Technological Institute of Optics, Colour and Imaging of Valencia - AIDO. Dr. Samuel Morillas acknowledges the support of Generalitat Valenciana under grant GVPRE/2008/257 and Universitat Politècnica de València under grant
Primeros Proyetos de InvestigaciĂłn 13202. }Oliver Moll, J.; Albiol Colomer, A.; Morillas, S.; Peris Fajarnes, G. (2011). A Hybrid Real-Time Vision-Based Person Detection Method. Waves. 86-95. http://hdl.handle.net/10251/57676S869
Nearest Labelset Using Double Distances for Multi-label Classification
Multi-label classification is a type of supervised learning where an instance
may belong to multiple labels simultaneously. Predicting each label
independently has been criticized for not exploiting any correlation between
labels. In this paper we propose a novel approach, Nearest Labelset using
Double Distances (NLDD), that predicts the labelset observed in the training
data that minimizes a weighted sum of the distances in both the feature space
and the label space to the new instance. The weights specify the relative
tradeoff between the two distances. The weights are estimated from a binomial
regression of the number of misclassified labels as a function of the two
distances. Model parameters are estimated by maximum likelihood. NLDD only
considers labelsets observed in the training data, thus implicitly taking into
account label dependencies. Experiments on benchmark multi-label data sets show
that the proposed method on average outperforms other well-known approaches in
terms of Hamming loss, 0/1 loss, and multi-label accuracy and ranks second
after ECC on the F-measure
Textual Query Based Image Retrieval
As digital cameras becoming popular and mobile phones are increased very fast so that consumers photos are increased. So that retrieving the appropriate image depending on content or text based image retrieval techniques has become very vast. Content-based image retrieval, a technique which uses visual contents to search images from large scale image databases according to users interests, has been an active and fast advancing research area semantic gap between the low-level visual features and the high-level semantic concepts. Real-time textual query-based personal photo retrieval system by leveraging millions of Web images and their associated rich textual descriptions. Then user provides a textual query. Our system generates the inverted file to automatically find the positive Web images that are related to the textual query as well as the negative Web images that are irrelevant to the textual query. For that purpose we use k-Nearest Neighbor (kNN), Decision stumps, and linear SVM, to rank personal photos. For improvement of the photo retrieval performance, we have used two relevance feedback methods via cross-domain learning, which effectively utilize both the Web images and personal images.
DOI: 10.17762/ijritcc2321-8169.15032
Shape recognition through multi-level fusion of features and classifiers
Shape recognition is a fundamental problem and a special type of image classification, where each shape is considered as a class. Current approaches to shape recognition mainly focus on designing low-level shape descriptors, and classify them using some machine learning approaches. In order to achieve effective learning of shape features, it is essential to ensure that a comprehensive set of high quality features can be extracted from the original shape data. Thus we have been motivated to develop methods of fusion of features and classifiers for advancing the classification performance. In this paper, we propose a multi-level framework for fusion of features and classifiers in the setting of gran-ular computing. The proposed framework involves creation of diversity among classifiers, through adopting feature selection and fusion to create diverse feature sets and to train diverse classifiers using different learn-Xinming Wang algorithms. The experimental results show that the proposed multi-level framework can effectively create diversity among classifiers leading to considerable advances in the classification performance
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