7,967 research outputs found
Search Tracker: Human-derived object tracking in-the-wild through large-scale search and retrieval
Humans use context and scene knowledge to easily localize moving objects in
conditions of complex illumination changes, scene clutter and occlusions. In
this paper, we present a method to leverage human knowledge in the form of
annotated video libraries in a novel search and retrieval based setting to
track objects in unseen video sequences. For every video sequence, a document
that represents motion information is generated. Documents of the unseen video
are queried against the library at multiple scales to find videos with similar
motion characteristics. This provides us with coarse localization of objects in
the unseen video. We further adapt these retrieved object locations to the new
video using an efficient warping scheme. The proposed method is validated on
in-the-wild video surveillance datasets where we outperform state-of-the-art
appearance-based trackers. We also introduce a new challenging dataset with
complex object appearance changes.Comment: Under review with the IEEE Transactions on Circuits and Systems for
Video Technolog
Towards an All-Purpose Content-Based Multimedia Information Retrieval System
The growth of multimedia collections - in terms of size, heterogeneity, and
variety of media types - necessitates systems that are able to conjointly deal
with several forms of media, especially when it comes to searching for
particular objects. However, existing retrieval systems are organized in silos
and treat different media types separately. As a consequence, retrieval across
media types is either not supported at all or subject to major limitations. In
this paper, we present vitrivr, a content-based multimedia information
retrieval stack. As opposed to the keyword search approach implemented by most
media management systems, vitrivr makes direct use of the object's content to
facilitate different types of similarity search, such as Query-by-Example or
Query-by-Sketch, for and, most importantly, across different media types -
namely, images, audio, videos, and 3D models. Furthermore, we introduce a new
web-based user interface that enables easy-to-use, multimodal retrieval from
and browsing in mixed media collections. The effectiveness of vitrivr is shown
on the basis of a user study that involves different query and media types. To
the best of our knowledge, the full vitrivr stack is unique in that it is the
first multimedia retrieval system that seamlessly integrates support for four
different types of media. As such, it paves the way towards an all-purpose,
content-based multimedia information retrieval system
Unsupervised Graph-based Rank Aggregation for Improved Retrieval
This paper presents a robust and comprehensive graph-based rank aggregation
approach, used to combine results of isolated ranker models in retrieval tasks.
The method follows an unsupervised scheme, which is independent of how the
isolated ranks are formulated. Our approach is able to combine arbitrary
models, defined in terms of different ranking criteria, such as those based on
textual, image or hybrid content representations.
We reformulate the ad-hoc retrieval problem as a document retrieval based on
fusion graphs, which we propose as a new unified representation model capable
of merging multiple ranks and expressing inter-relationships of retrieval
results automatically. By doing so, we claim that the retrieval system can
benefit from learning the manifold structure of datasets, thus leading to more
effective results. Another contribution is that our graph-based aggregation
formulation, unlike existing approaches, allows for encapsulating contextual
information encoded from multiple ranks, which can be directly used for
ranking, without further computations and post-processing steps over the
graphs. Based on the graphs, a novel similarity retrieval score is formulated
using an efficient computation of minimum common subgraphs. Finally, another
benefit over existing approaches is the absence of hyperparameters.
A comprehensive experimental evaluation was conducted considering diverse
well-known public datasets, composed of textual, image, and multimodal
documents. Performed experiments demonstrate that our method reaches top
performance, yielding better effectiveness scores than state-of-the-art
baseline methods and promoting large gains over the rankers being fused, thus
demonstrating the successful capability of the proposal in representing queries
based on a unified graph-based model of rank fusions
AXES at TRECVID 2012: KIS, INS, and MED
The AXES project participated in the interactive instance search task (INS), the known-item search task (KIS), and the multimedia event detection task (MED) for TRECVid 2012. As in our TRECVid 2011 system, we used nearly identical search systems and user interfaces for both INS and KIS. Our interactive INS and KIS systems focused this year on using classifiers trained at query time with positive examples collected from external search engines. Participants in our KIS experiments were media professionals from the BBC; our INS experiments were carried out by students and researchers at Dublin City University. We performed comparatively well in both experiments. Our best KIS run found 13 of the 25 topics, and our best INS runs outperformed all other submitted runs in terms of P@100. For MED, the system presented was based on a minimal number of low-level descriptors, which we chose to be as large as computationally feasible. These descriptors are aggregated to produce high-dimensional video-level signatures, which are used to train a set of linear classifiers. Our MED system achieved the second-best score of all submitted runs in the main track, and best score in the ad-hoc track, suggesting that a simple system based on state-of-the-art low-level descriptors can give relatively high performance. This paper describes in detail our KIS, INS, and MED systems and the results and findings of our experiments
Strategies for Searching Video Content with Text Queries or Video Examples
The large number of user-generated videos uploaded on to the Internet
everyday has led to many commercial video search engines, which mainly rely on
text metadata for search. However, metadata is often lacking for user-generated
videos, thus these videos are unsearchable by current search engines.
Therefore, content-based video retrieval (CBVR) tackles this metadata-scarcity
problem by directly analyzing the visual and audio streams of each video. CBVR
encompasses multiple research topics, including low-level feature design,
feature fusion, semantic detector training and video search/reranking. We
present novel strategies in these topics to enhance CBVR in both accuracy and
speed under different query inputs, including pure textual queries and query by
video examples. Our proposed strategies have been incorporated into our
submission for the TRECVID 2014 Multimedia Event Detection evaluation, where
our system outperformed other submissions in both text queries and video
example queries, thus demonstrating the effectiveness of our proposed
approaches
Discrete Multi-modal Hashing with Canonical Views for Robust Mobile Landmark Search
Mobile landmark search (MLS) recently receives increasing attention for its
great practical values. However, it still remains unsolved due to two important
challenges. One is high bandwidth consumption of query transmission, and the
other is the huge visual variations of query images sent from mobile devices.
In this paper, we propose a novel hashing scheme, named as canonical view based
discrete multi-modal hashing (CV-DMH), to handle these problems via a novel
three-stage learning procedure. First, a submodular function is designed to
measure visual representativeness and redundancy of a view set. With it,
canonical views, which capture key visual appearances of landmark with limited
redundancy, are efficiently discovered with an iterative mining strategy.
Second, multi-modal sparse coding is applied to transform visual features from
multiple modalities into an intermediate representation. It can robustly and
adaptively characterize visual contents of varied landmark images with certain
canonical views. Finally, compact binary codes are learned on intermediate
representation within a tailored discrete binary embedding model which
preserves visual relations of images measured with canonical views and removes
the involved noises. In this part, we develop a new augmented Lagrangian
multiplier (ALM) based optimization method to directly solve the discrete
binary codes. We can not only explicitly deal with the discrete constraint, but
also consider the bit-uncorrelated constraint and balance constraint together.
Experiments on real world landmark datasets demonstrate the superior
performance of CV-DMH over several state-of-the-art methods
Physical Representation-based Predicate Optimization for a Visual Analytics Database
Querying the content of images, video, and other non-textual data sources
requires expensive content extraction methods. Modern extraction techniques are
based on deep convolutional neural networks (CNNs) and can classify objects
within images with astounding accuracy. Unfortunately, these methods are slow:
processing a single image can take about 10 milliseconds on modern GPU-based
hardware. As massive video libraries become ubiquitous, running a content-based
query over millions of video frames is prohibitive.
One promising approach to reduce the runtime cost of queries of visual
content is to use a hierarchical model, such as a cascade, where simple cases
are handled by an inexpensive classifier. Prior work has sought to design
cascades that optimize the computational cost of inference by, for example,
using smaller CNNs. However, we observe that there are critical factors besides
the inference time that dramatically impact the overall query time. Notably, by
treating the physical representation of the input image as part of our query
optimization---that is, by including image transforms, such as resolution
scaling or color-depth reduction, within the cascade---we can optimize data
handling costs and enable drastically more efficient classifier cascades.
In this paper, we propose Tahoma, which generates and evaluates many
potential classifier cascades that jointly optimize the CNN architecture and
input data representation. Our experiments on a subset of ImageNet show that
Tahoma's input transformations speed up cascades by up to 35 times. We also
find up to a 98x speedup over the ResNet50 classifier with no loss in accuracy,
and a 280x speedup if some accuracy is sacrificed.Comment: Camera-ready version of the paper submitted to ICDE 2019, In
Proceedings of the 35th IEEE International Conference on Data Engineering
(ICDE 2019
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