530,177 research outputs found
SVS-JOIN : efficient spatial visual similarity join for geo-multimedia
In the big data era, massive amount of multimedia data with geo-tags has been generated and collected by smart devices equipped with mobile communications module and position sensor module. This trend has put forward higher request on large-scale geo-multimedia retrieval. Spatial similarity join is one of the significant problems in the area of spatial database. Previous works focused on spatial textual document search problem, rather than geo-multimedia retrieval. In this paper, we investigate a novel geo-multimedia retrieval paradigm named spatial visual similarity join (SVS-JOIN for short), which aims to search similar geo-image pairs in both aspects of geo-location and visual content. Firstly, the definition of SVS-JOIN is proposed and then we present the geographical similarity and visual similarity measurement. Inspired by the approach for textual similarity join, we develop an algorithm named SVS-JOIN B by combining the PPJOIN algorithm and visual similarity. Besides, an extension of it named SVS-JOIN G is developed, which utilizes spatial grid strategy to improve the search efficiency. To further speed up the search, a novel approach called SVS-JOIN Q is carefully designed, in which a quadtree and a global inverted index are employed. Comprehensive experiments are conducted on two geo-image datasets and the results demonstrate that our solution can address the SVS-JOIN problem effectively and efficiently
Combining EEG source connectivity and network similarity: Application to object categorization in the human brain
A major challenge in cognitive neuroscience is to evaluate the ability of the
human brain to categorize or group visual stimuli based on common features.
This categorization process is very fast and occurs in few hundreds of
millisecond time scale. However, an accurate tracking of the spatiotemporal
dynamics of large-scale brain networks is still an unsolved issue. Here, we
show the combination of recently developed method called dense-EEG source
connectivity to identify functional brain networks with excellent temporal and
spatial resolutions and an algorithm, called SimNet, to compute brain networks
similarity. Two categories of visual stimuli were analysed in this study:
immobile and mobile. Networks similarity was assessed within each category
(intra-condition) and between categories (inter-condition). Results showed high
similarity within each category and low similarity between the two categories.
A significant difference between similarities computed in the intra and
inter-conditions was observed at the period of 120-190ms supposed to be related
to visual recognition and memory access. We speculate that these observations
will be very helpful toward understanding the object categorization in the
human brain from a network perspective.Comment: 5 pages, 2 figures. Accepted for 2016 IEEE Workshop on Statistical
Signal Processin
Context Embedding Networks
Low dimensional embeddings that capture the main variations of interest in
collections of data are important for many applications. One way to construct
these embeddings is to acquire estimates of similarity from the crowd. However,
similarity is a multi-dimensional concept that varies from individual to
individual. Existing models for learning embeddings from the crowd typically
make simplifying assumptions such as all individuals estimate similarity using
the same criteria, the list of criteria is known in advance, or that the crowd
workers are not influenced by the data that they see. To overcome these
limitations we introduce Context Embedding Networks (CENs). In addition to
learning interpretable embeddings from images, CENs also model worker biases
for different attributes along with the visual context i.e. the visual
attributes highlighted by a set of images. Experiments on two noisy crowd
annotated datasets show that modeling both worker bias and visual context
results in more interpretable embeddings compared to existing approaches.Comment: CVPR 2018 spotligh
Hierarchy-based Image Embeddings for Semantic Image Retrieval
Deep neural networks trained for classification have been found to learn
powerful image representations, which are also often used for other tasks such
as comparing images w.r.t. their visual similarity. However, visual similarity
does not imply semantic similarity. In order to learn semantically
discriminative features, we propose to map images onto class embeddings whose
pair-wise dot products correspond to a measure of semantic similarity between
classes. Such an embedding does not only improve image retrieval results, but
could also facilitate integrating semantics for other tasks, e.g., novelty
detection or few-shot learning. We introduce a deterministic algorithm for
computing the class centroids directly based on prior world-knowledge encoded
in a hierarchy of classes such as WordNet. Experiments on CIFAR-100, NABirds,
and ImageNet show that our learned semantic image embeddings improve the
semantic consistency of image retrieval results by a large margin.Comment: Accepted at WACV 2019. Source code:
https://github.com/cvjena/semantic-embedding
- …