2,823 research outputs found
Cross-Bifix-Free Codes Within a Constant Factor of Optimality
A cross-bifix-free code is a set of words in which no prefix of any length of
any word is the suffix of any word in the set. Cross-bifix-free codes arise in
the study of distributed sequences for frame synchronization. We provide a new
construction of cross-bifix-free codes which generalizes the construction in
Bajic (2007) to longer code lengths and to any alphabet size. The codes are
shown to be nearly optimal in size. We also establish new results on Fibonacci
sequences, that are used in estimating the size of the cross-bifix-free codes
Weakly-Supervised Alignment of Video With Text
Suppose that we are given a set of videos, along with natural language
descriptions in the form of multiple sentences (e.g., manual annotations, movie
scripts, sport summaries etc.), and that these sentences appear in the same
temporal order as their visual counterparts. We propose in this paper a method
for aligning the two modalities, i.e., automatically providing a time stamp for
every sentence. Given vectorial features for both video and text, we propose to
cast this task as a temporal assignment problem, with an implicit linear
mapping between the two feature modalities. We formulate this problem as an
integer quadratic program, and solve its continuous convex relaxation using an
efficient conditional gradient algorithm. Several rounding procedures are
proposed to construct the final integer solution. After demonstrating
significant improvements over the state of the art on the related task of
aligning video with symbolic labels [7], we evaluate our method on a
challenging dataset of videos with associated textual descriptions [36], using
both bag-of-words and continuous representations for text.Comment: ICCV 2015 - IEEE International Conference on Computer Vision, Dec
2015, Santiago, Chil
Coarse-to-Fine Adaptive People Detection for Video Sequences by Maximizing Mutual Information
Applying people detectors to unseen data is challenging since patterns distributions, such
as viewpoints, motion, poses, backgrounds, occlusions and people sizes, may significantly differ
from the ones of the training dataset. In this paper, we propose a coarse-to-fine framework to adapt
frame by frame people detectors during runtime classification, without requiring any additional
manually labeled ground truth apart from the offline training of the detection model. Such adaptation
make use of multiple detectors mutual information, i.e., similarities and dissimilarities of detectors
estimated and agreed by pair-wise correlating their outputs. Globally, the proposed adaptation
discriminates between relevant instants in a video sequence, i.e., identifies the representative frames
for an adaptation of the system. Locally, the proposed adaptation identifies the best configuration
(i.e., detection threshold) of each detector under analysis, maximizing the mutual information to
obtain the detection threshold of each detector. The proposed coarse-to-fine approach does not
require training the detectors for each new scenario and uses standard people detector outputs, i.e.,
bounding boxes. The experimental results demonstrate that the proposed approach outperforms
state-of-the-art detectors whose optimal threshold configurations are previously determined and
fixed from offline training dataThis work has been partially supported by the Spanish government under the project TEC2014-53176-R
(HAVideo
Advances on Time Series Analysis using Elastic Measures of Similarity
A sequence is a collection of data instances arranged in a structured manner. When this arrangement is held in the time domain, sequences are instead referred to as time series. As such, each observation in a time series represents an observation drawn from an underlying process, produced at a specific time instant. However, other type of data indexing structures, such as space- or threshold-based arrangements are possible. Data points that compose a time series are often correlated with each other. To account for this correlation in data mining tasks, time series are usually studied as a whole data object rather than as a collection of independent observations. In this context, techniques for time series analysis aim at analyzing this type of data structures by applying specific approaches developed to leverage intrinsic properties of the time series for a wide range of problems, such as classification, clustering and other tasks alike.
The development of monitoring and storage devices has made time se- ries analysis proliferate in numerous application fields, including medicine, economics, manufacturing and telecommunications, among others. Over the years, the community has gathered efforts towards the development of new data-based techniques for time series analysis suited to address the problems and needs of such application fields. In the related literature, such techniques can be divided in three main groups: feature-, model- and distance-based methods. The first group (feature-based) transforms time series into a collection of features, which are then used by conventional learning algorithms to provide solutions to the task under consideration. In contrast, methods belonging to the second group (model-based) assume that each time series is drawn from a generative model, which is then har- nessed to elicit knowledge from data. Finally, distance-based techniques operate directly on raw time series. To this end, these methods resort to specially defined measures of distance or similarity for comparing time series, without requiring any further processing. Among them, elastic sim- ilarity measures (e.g., dynamic time warping and edit distance) compute the closeness between two sequences by finding the best alignment between them, disregarding differences in time, and thus focusing exclusively on shape differences.
This Thesis presents several contributions to the field of distance-based techniques for time series analysis, namely: i) a novel multi-dimensional elastic similarity learning method for time series classification; ii) an adap- tation of elastic measures to streaming time series scenarios; and iii) the use of distance-based time series analysis to make machine learning meth- ods for image classification robust against adversarial attacks. Throughout the Thesis, each contribution is framed within its related state of the art, explained in detail and empirically evaluated. The obtained results lead to new insights on the application of distance-based time series methods for the considered scenarios, and motivates research directions that highlight the vibrant momentum of this research area
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