7,829 research outputs found
Evaluation of color representation for texture analysis
Since more than 50 years texture in image material is a topic of research. Hereby, color was ignored mostly. This study compares 70 different configurations for texture analysis, using four features. For the configurations we used: (i) a gray value texture descriptor: the co-occurrence matrix and a color texture descriptor: the color correlogram, (ii) six color spaces, and (iii) several quantization schemes. A three classifier combination was used to classify the output of the configurations on the VisTex texture database. The results indicate that the use of a coarse HSV color space quantization can substantially improve texture recognition compared to various other gray and color quantization schemes
Audio Classification from Time-Frequency Texture
Time-frequency representations of audio signals often resemble texture
images. This paper derives a simple audio classification algorithm based on
treating sound spectrograms as texture images. The algorithm is inspired by an
earlier visual classification scheme particularly efficient at classifying
textures. While solely based on time-frequency texture features, the algorithm
achieves surprisingly good performance in musical instrument classification
experiments
Perception Driven Texture Generation
This paper investigates a novel task of generating texture images from
perceptual descriptions. Previous work on texture generation focused on either
synthesis from examples or generation from procedural models. Generating
textures from perceptual attributes have not been well studied yet. Meanwhile,
perceptual attributes, such as directionality, regularity and roughness are
important factors for human observers to describe a texture. In this paper, we
propose a joint deep network model that combines adversarial training and
perceptual feature regression for texture generation, while only random noise
and user-defined perceptual attributes are required as input. In this model, a
preliminary trained convolutional neural network is essentially integrated with
the adversarial framework, which can drive the generated textures to possess
given perceptual attributes. An important aspect of the proposed model is that,
if we change one of the input perceptual features, the corresponding appearance
of the generated textures will also be changed. We design several experiments
to validate the effectiveness of the proposed method. The results show that the
proposed method can produce high quality texture images with desired perceptual
properties.Comment: 7 pages, 4 figures, icme201
Semantics-Aligned Representation Learning for Person Re-identification
Person re-identification (reID) aims to match person images to retrieve the
ones with the same identity. This is a challenging task, as the images to be
matched are generally semantically misaligned due to the diversity of human
poses and capture viewpoints, incompleteness of the visible bodies (due to
occlusion), etc. In this paper, we propose a framework that drives the reID
network to learn semantics-aligned feature representation through delicate
supervision designs. Specifically, we build a Semantics Aligning Network (SAN)
which consists of a base network as encoder (SA-Enc) for re-ID, and a decoder
(SA-Dec) for reconstructing/regressing the densely semantics aligned full
texture image. We jointly train the SAN under the supervisions of person
re-identification and aligned texture generation. Moreover, at the decoder,
besides the reconstruction loss, we add Triplet ReID constraints over the
feature maps as the perceptual losses. The decoder is discarded in the
inference and thus our scheme is computationally efficient. Ablation studies
demonstrate the effectiveness of our design. We achieve the state-of-the-art
performances on the benchmark datasets CUHK03, Market1501, MSMT17, and the
partial person reID dataset Partial REID. Code for our proposed method is
available at:
https://github.com/microsoft/Semantics-Aligned-Representation-Learning-for-Person-Re-identification.Comment: Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20),
code has been release
Detecting the presence of large buildings in natural images
This paper addresses the issue of classification of lowlevel
features into high-level semantic concepts for the purpose of semantic annotation of consumer photographs. We adopt a multi-scale approach that relies on edge detection to extract an edge orientation-based feature description of the image, and apply an SVM learning technique to infer the presence of a dominant building object in a general purpose collection of digital photographs. The approach exploits prior knowledge on the image context through an assumption that all input images are �outdoor�, i.e. indoor/outdoor classification (the context determination stage) has been performed. The proposed approach is validated on a diverse dataset of 1720 images and its performance compared with that of the MPEG-7 edge histogram descriptor
Extended pipeline for content-based feature engineering in music genre recognition
We present a feature engineering pipeline for the construction of musical
signal characteristics, to be used for the design of a supervised model for
musical genre identification. The key idea is to extend the traditional
two-step process of extraction and classification with additive stand-alone
phases which are no longer organized in a waterfall scheme. The whole system is
realized by traversing backtrack arrows and cycles between various stages. In
order to give a compact and effective representation of the features, the
standard early temporal integration is combined with other selection and
extraction phases: on the one hand, the selection of the most meaningful
characteristics based on information gain, and on the other hand, the inclusion
of the nonlinear correlation between this subset of features, determined by an
autoencoder. The results of the experiments conducted on GTZAN dataset reveal a
noticeable contribution of this methodology towards the model's performance in
classification task.Comment: ICASSP 201
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