34,279 research outputs found
Measuring concept similarities in multimedia ontologies: analysis and evaluations
The recent development of large-scale multimedia concept ontologies has provided a new momentum for research in the semantic analysis of multimedia repositories. Different methods for generic concept detection have been extensively studied, but the question of how to exploit the structure of a multimedia ontology and existing inter-concept relations has not received similar attention. In this paper, we present a clustering-based method for modeling semantic concepts on low-level feature spaces and study the evaluation of the quality of such models with entropy-based methods. We cover a variety of methods for assessing the similarity of different concepts in a multimedia ontology. We study three ontologies and apply the proposed techniques in experiments involving the visual and semantic similarities, manual annotation of video, and concept detection. The results show that modeling inter-concept relations can provide a promising resource for many different application areas in semantic multimedia processing
Semantic spaces revisited: investigating the performance of auto-annotation and semantic retrieval using semantic spaces
Semantic spaces encode similarity relationships between objects as a function of position in a mathematical space. This paper discusses three different formulations for building semantic spaces which allow the automatic-annotation and semantic retrieval of images. The models discussed in this paper require that the image content be described in the form of a series of visual-terms, rather than as a continuous feature-vector. The paper also discusses how these term-based models compare to the latest state-of-the-art continuous feature models for auto-annotation and retrieval
Review of Person Re-identification Techniques
Person re-identification across different surveillance cameras with disjoint
fields of view has become one of the most interesting and challenging subjects
in the area of intelligent video surveillance. Although several methods have
been developed and proposed, certain limitations and unresolved issues remain.
In all of the existing re-identification approaches, feature vectors are
extracted from segmented still images or video frames. Different similarity or
dissimilarity measures have been applied to these vectors. Some methods have
used simple constant metrics, whereas others have utilised models to obtain
optimised metrics. Some have created models based on local colour or texture
information, and others have built models based on the gait of people. In
general, the main objective of all these approaches is to achieve a
higher-accuracy rate and lowercomputational costs. This study summarises
several developments in recent literature and discusses the various available
methods used in person re-identification. Specifically, their advantages and
disadvantages are mentioned and compared.Comment: Published 201
A Review of Codebook Models in Patch-Based Visual Object Recognition
The codebook model-based approach, while ignoring any structural aspect in vision, nonetheless provides state-of-the-art performances on current datasets. The key role of a visual codebook is to provide a way to map the low-level features into a fixed-length vector in histogram space to which standard classifiers can be directly applied. The discriminative power of such a visual codebook determines the quality of the codebook model, whereas the size of the codebook controls the complexity of the model. Thus, the construction of a codebook is an important step which is usually done by cluster analysis. However, clustering is a process that retains regions of high density in a distribution and it follows that the resulting codebook need not have discriminant properties. This is also recognised as a computational bottleneck of such systems. In our recent work, we proposed a resource-allocating codebook, to constructing a discriminant codebook in a one-pass design procedure that slightly outperforms more traditional approaches at drastically reduced computing times. In this review we survey several approaches that have been proposed over the last decade with their use of feature detectors, descriptors, codebook construction schemes, choice of classifiers in recognising objects, and datasets that were used in evaluating the proposed methods
Large Scale Visual Recommendations From Street Fashion Images
We describe a completely automated large scale visual recommendation system
for fashion. Our focus is to efficiently harness the availability of large
quantities of online fashion images and their rich meta-data. Specifically, we
propose four data driven models in the form of Complementary Nearest Neighbor
Consensus, Gaussian Mixture Models, Texture Agnostic Retrieval and Markov Chain
LDA for solving this problem. We analyze relative merits and pitfalls of these
algorithms through extensive experimentation on a large-scale data set and
baseline them against existing ideas from color science. We also illustrate key
fashion insights learned through these experiments and show how they can be
employed to design better recommendation systems. Finally, we also outline a
large-scale annotated data set of fashion images (Fashion-136K) that can be
exploited for future vision research
Rejection-Cascade of Gaussians: Real-time adaptive background subtraction framework
Background-Foreground classification is a well-studied problem in computer
vision. Due to the pixel-wise nature of modeling and processing in the
algorithm, it is usually difficult to satisfy real-time constraints. There is a
trade-off between the speed (because of model complexity) and accuracy.
Inspired by the rejection cascade of Viola-Jones classifier, we decompose the
Gaussian Mixture Model (GMM) into an adaptive cascade of Gaussians(CoG). We
achieve a good improvement in speed without compromising the accuracy with
respect to the baseline GMM model. We demonstrate a speed-up factor of 4-5x and
17 percent average improvement in accuracy over Wallflowers surveillance
datasets. The CoG is then demonstrated to over the latent space representation
of images of a convolutional variational autoencoder(VAE). We provide initial
results over CDW-2014 dataset, which could speed up background subtraction for
deep architectures.Comment: Accepted for National Conference on Computer Vision, Pattern
Recognition, Image Processing and Graphics (NCVPRIPG 2019
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