11,824 research outputs found
Probability Weighted Compact Feature for Domain Adaptive Retrieval
Domain adaptive image retrieval includes single-domain retrieval and
cross-domain retrieval. Most of the existing image retrieval methods only focus
on single-domain retrieval, which assumes that the distributions of retrieval
databases and queries are similar. However, in practical application, the
discrepancies between retrieval databases often taken in ideal
illumination/pose/background/camera conditions and queries usually obtained in
uncontrolled conditions are very large. In this paper, considering the
practical application, we focus on challenging cross-domain retrieval. To
address the problem, we propose an effective method named Probability Weighted
Compact Feature Learning (PWCF), which provides inter-domain correlation
guidance to promote cross-domain retrieval accuracy and learns a series of
compact binary codes to improve the retrieval speed. First, we derive our loss
function through the Maximum A Posteriori Estimation (MAP): Bayesian
Perspective (BP) induced focal-triplet loss, BP induced quantization loss and
BP induced classification loss. Second, we propose a common manifold structure
between domains to explore the potential correlation across domains.
Considering the original feature representation is biased due to the
inter-domain discrepancy, the manifold structure is difficult to be
constructed. Therefore, we propose a new feature named Histogram Feature of
Neighbors (HFON) from the sample statistics perspective. Extensive experiments
on various benchmark databases validate that our method outperforms many
state-of-the-art image retrieval methods for domain adaptive image retrieval.
The source code is available at https://github.com/fuxianghuang1/PWCFComment: Accepted by CVPR 2020; The source code is available at
https://github.com/fuxianghuang1/PWC
An agent-driven semantical identifier using radial basis neural networks and reinforcement learning
Due to the huge availability of documents in digital form, and the deception
possibility raise bound to the essence of digital documents and the way they
are spread, the authorship attribution problem has constantly increased its
relevance. Nowadays, authorship attribution,for both information retrieval and
analysis, has gained great importance in the context of security, trust and
copyright preservation. This work proposes an innovative multi-agent driven
machine learning technique that has been developed for authorship attribution.
By means of a preprocessing for word-grouping and time-period related analysis
of the common lexicon, we determine a bias reference level for the recurrence
frequency of the words within analysed texts, and then train a Radial Basis
Neural Networks (RBPNN)-based classifier to identify the correct author. The
main advantage of the proposed approach lies in the generality of the semantic
analysis, which can be applied to different contexts and lexical domains,
without requiring any modification. Moreover, the proposed system is able to
incorporate an external input, meant to tune the classifier, and then
self-adjust by means of continuous learning reinforcement.Comment: Published on: Proceedings of the XV Workshop "Dagli Oggetti agli
Agenti" (WOA 2014), Catania, Italy, Sepember. 25-26, 201
On User Modelling for Personalised News Video Recommendation
In this paper, we introduce a novel approach for modelling user interests. Our approach captures users evolving information needs, identifies aspects of their need and recommends relevant news items to the users. We introduce our approach within the context of personalised news video retrieval. A news video data set is used for experimentation. We employ a simulated user evaluation
Adaptive Graph via Multiple Kernel Learning for Nonnegative Matrix Factorization
Nonnegative Matrix Factorization (NMF) has been continuously evolving in
several areas like pattern recognition and information retrieval methods. It
factorizes a matrix into a product of 2 low-rank non-negative matrices that
will define parts-based, and linear representation of nonnegative data.
Recently, Graph regularized NMF (GrNMF) is proposed to find a compact
representation,which uncovers the hidden semantics and simultaneously respects
the intrinsic geometric structure. In GNMF, an affinity graph is constructed
from the original data space to encode the geometrical information. In this
paper, we propose a novel idea which engages a Multiple Kernel Learning
approach into refining the graph structure that reflects the factorization of
the matrix and the new data space. The GrNMF is improved by utilizing the graph
refined by the kernel learning, and then a novel kernel learning method is
introduced under the GrNMF framework. Our approach shows encouraging results of
the proposed algorithm in comparison to the state-of-the-art clustering
algorithms like NMF, GrNMF, SVD etc.Comment: This paper has been withdrawn by the author due to the terrible
writin
Hybrid image representation methods for automatic image annotation: a survey
In most automatic image annotation systems, images are represented with low level features using either global
methods or local methods. In global methods, the entire image is used as a unit. Local methods divide images into blocks where fixed-size sub-image blocks are adopted as sub-units; or into regions by using segmented regions as sub-units in images. In contrast to typical automatic image annotation methods that use either global or local features exclusively, several recent methods have considered incorporating the two kinds of information, and believe that the combination of the two levels of features is
beneficial in annotating images. In this paper, we provide a
survey on automatic image annotation techniques according to
one aspect: feature extraction, and, in order to complement
existing surveys in literature, we focus on the emerging image annotation methods: hybrid methods that combine both global and local features for image representation
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