31,175 research outputs found
Deformable Prototypes for Encoding Shape Categories in Image Databases
We describe a method for shape-based image database search that uses deformable prototypes to represent categories. Rather than directly comparing a candidate shape with all shape entries in the database, shapes are compared in terms of the types of nonrigid deformations (differences) that relate them to a small subset of representative prototypes. To solve the shape correspondence and alignment problem, we employ the technique of modal matching, an information-preserving shape decomposition for matching, describing, and comparing shapes despite sensor variations and nonrigid deformations. In modal matching, shape is decomposed into an ordered basis of orthogonal principal components. We demonstrate the utility of this approach for shape comparison in 2-D image databases.Office of Naval Research (Young Investigator Award N00014-06-1-0661
Interactive retrieval of video using pre-computed shot-shot similarities
A probabilistic framework for content-based interactive video retrieval is described. The developed indexing of video fragments originates from the probability of the user's positive judgment about key-frames of video shots. Initial estimates of the probabilities are obtained from low-level feature representation. Only statistically significant estimates are picked out, the rest are replaced by an appropriate constant allowing efficient access at search time without loss of search quality and leading to improvement in most experiments. With time, these probability estimates are updated from the relevance judgment of users performing searches, resulting in further substantial increases in mean average precision
Learning Temporal Alignment Uncertainty for Efficient Event Detection
In this paper we tackle the problem of efficient video event detection. We
argue that linear detection functions should be preferred in this regard due to
their scalability and efficiency during estimation and evaluation. A popular
approach in this regard is to represent a sequence using a bag of words (BOW)
representation due to its: (i) fixed dimensionality irrespective of the
sequence length, and (ii) its ability to compactly model the statistics in the
sequence. A drawback to the BOW representation, however, is the intrinsic
destruction of the temporal ordering information. In this paper we propose a
new representation that leverages the uncertainty in relative temporal
alignments between pairs of sequences while not destroying temporal ordering.
Our representation, like BOW, is of a fixed dimensionality making it easily
integrated with a linear detection function. Extensive experiments on CK+,
6DMG, and UvA-NEMO databases show significant performance improvements across
both isolated and continuous event detection tasks.Comment: Appeared in DICTA 2015, 8 page
Extraction of the underlying structure of systematic risk from non-Gaussian multivariate financial time series using independent component analysis: Evidence from the Mexican stock exchange
Regarding the problems related to multivariate non-Gaussianity of financial time series, i.e., unreliable results in extraction of underlying risk factors -via Principal Component Analysis or Factor Analysis-, we use Independent Component Analysis (ICA) to estimate the pervasive risk factors that explain the returns on stocks in the Mexican Stock Exchange. The extracted systematic risk factors are considered within a statistical definition of the Arbitrage Pricing Theory (APT), which is tested by means of a two-stage econometric methodology. Using the extracted factors, we find evidence of a suitable estimation via ICA and some results in favor of the APT.Peer ReviewedPostprint (published version
Sequential Complexity as a Descriptor for Musical Similarity
We propose string compressibility as a descriptor of temporal structure in
audio, for the purpose of determining musical similarity. Our descriptors are
based on computing track-wise compression rates of quantised audio features,
using multiple temporal resolutions and quantisation granularities. To verify
that our descriptors capture musically relevant information, we incorporate our
descriptors into similarity rating prediction and song year prediction tasks.
We base our evaluation on a dataset of 15500 track excerpts of Western popular
music, for which we obtain 7800 web-sourced pairwise similarity ratings. To
assess the agreement among similarity ratings, we perform an evaluation under
controlled conditions, obtaining a rank correlation of 0.33 between intersected
sets of ratings. Combined with bag-of-features descriptors, we obtain
performance gains of 31.1% and 10.9% for similarity rating prediction and song
year prediction. For both tasks, analysis of selected descriptors reveals that
representing features at multiple time scales benefits prediction accuracy.Comment: 13 pages, 9 figures, 8 tables. Accepted versio
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