65,977 research outputs found
Person Re-Identification by Deep Joint Learning of Multi-Loss Classification
Existing person re-identification (re-id) methods rely mostly on either
localised or global feature representation alone. This ignores their joint
benefit and mutual complementary effects. In this work, we show the advantages
of jointly learning local and global features in a Convolutional Neural Network
(CNN) by aiming to discover correlated local and global features in different
context. Specifically, we formulate a method for joint learning of local and
global feature selection losses designed to optimise person re-id when using
only generic matching metrics such as the L2 distance. We design a novel CNN
architecture for Jointly Learning Multi-Loss (JLML) of local and global
discriminative feature optimisation subject concurrently to the same re-id
labelled information. Extensive comparative evaluations demonstrate the
advantages of this new JLML model for person re-id over a wide range of
state-of-the-art re-id methods on five benchmarks (VIPeR, GRID, CUHK01, CUHK03,
Market-1501).Comment: Accepted by IJCAI 201
Intra-Camera Supervised Person Re-Identification: A New Benchmark
Existing person re-identification (re-id) methods rely mostly on a large set
of inter-camera identity labelled training data, requiring a tedious data
collection and annotation process therefore leading to poor scalability in
practical re-id applications. To overcome this fundamental limitation, we
consider person re-identification without inter-camera identity association but
only with identity labels independently annotated within each individual
camera-view. This eliminates the most time-consuming and tedious inter-camera
identity labelling process in order to significantly reduce the amount of human
efforts required during annotation. It hence gives rise to a more scalable and
more feasible learning scenario, which we call Intra-Camera Supervised (ICS)
person re-id. Under this ICS setting with weaker label supervision, we
formulate a Multi-Task Multi-Label (MTML) deep learning method. Given no
inter-camera association, MTML is specially designed for self-discovering the
inter-camera identity correspondence. This is achieved by inter-camera
multi-label learning under a joint multi-task inference framework. In addition,
MTML can also efficiently learn the discriminative re-id feature
representations by fully using the available identity labels within each
camera-view. Extensive experiments demonstrate the performance superiority of
our MTML model over the state-of-the-art alternative methods on three
large-scale person re-id datasets in the proposed intra-camera supervised
learning setting.Comment: 9 pages, 3 figures, accepted by ICCV Workshop on Real-World
Recognition from Low-Quality Images and Videos, 201
Classification and Verification of Online Handwritten Signatures with Time Causal Information Theory Quantifiers
We present a new approach for online handwritten signature classification and
verification based on descriptors stemming from Information Theory. The
proposal uses the Shannon Entropy, the Statistical Complexity, and the Fisher
Information evaluated over the Bandt and Pompe symbolization of the horizontal
and vertical coordinates of signatures. These six features are easy and fast to
compute, and they are the input to an One-Class Support Vector Machine
classifier. The results produced surpass state-of-the-art techniques that
employ higher-dimensional feature spaces which often require specialized
software and hardware. We assess the consistency of our proposal with respect
to the size of the training sample, and we also use it to classify the
signatures into meaningful groups.Comment: Submitted to PLOS On
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