1,315 research outputs found
Beyond Classification: Latent User Interests Profiling from Visual Contents Analysis
User preference profiling is an important task in modern online social
networks (OSN). With the proliferation of image-centric social platforms, such
as Pinterest, visual contents have become one of the most informative data
streams for understanding user preferences. Traditional approaches usually
treat visual content analysis as a general classification problem where one or
more labels are assigned to each image. Although such an approach simplifies
the process of image analysis, it misses the rich context and visual cues that
play an important role in people's perception of images. In this paper, we
explore the possibilities of learning a user's latent visual preferences
directly from image contents. We propose a distance metric learning method
based on Deep Convolutional Neural Networks (CNN) to directly extract
similarity information from visual contents and use the derived distance metric
to mine individual users' fine-grained visual preferences. Through our
preliminary experiments using data from 5,790 Pinterest users, we show that
even for the images within the same category, each user possesses distinct and
individually-identifiable visual preferences that are consistent over their
lifetime. Our results underscore the untapped potential of finer-grained visual
preference profiling in understanding users' preferences.Comment: 2015 IEEE 15th International Conference on Data Mining Workshop
Forecasting with time series imaging
Feature-based time series representations have attracted substantial
attention in a wide range of time series analysis methods. Recently, the use of
time series features for forecast model averaging has been an emerging research
focus in the forecasting community. Nonetheless, most of the existing
approaches depend on the manual choice of an appropriate set of features.
Exploiting machine learning methods to extract features from time series
automatically becomes crucial in state-of-the-art time series analysis. In this
paper, we introduce an automated approach to extract time series features based
on time series imaging. We first transform time series into recurrence plots,
from which local features can be extracted using computer vision algorithms.
The extracted features are used for forecast model averaging. Our experiments
show that forecasting based on automatically extracted features, with less
human intervention and a more comprehensive view of the raw time series data,
yields highly comparable performances with the best methods in the largest
forecasting competition dataset (M4) and outperforms the top methods in the
Tourism forecasting competition dataset
CAPS: A Practical Partition Index for Filtered Similarity Search
With the surging popularity of approximate near-neighbor search (ANNS),
driven by advances in neural representation learning, the ability to serve
queries accompanied by a set of constraints has become an area of intense
interest. While the community has recently proposed several algorithms for
constrained ANNS, almost all of these methods focus on integration with
graph-based indexes, the predominant class of algorithms achieving
state-of-the-art performance in latency-recall tradeoffs. In this work, we take
a different approach and focus on developing a constrained ANNS algorithm via
space partitioning as opposed to graphs. To that end, we introduce Constrained
Approximate Partitioned Search (CAPS), an index for ANNS with filters via space
partitions that not only retains the benefits of a partition-based algorithm
but also outperforms state-of-the-art graph-based constrained search techniques
in recall-latency tradeoffs, with only 10% of the index size.Comment: 14 page
Voronoi-Based Compact Image Descriptors: Efficient Region-of-Interest Retrieval With VLAD and Deep-Learning-Based Descriptors
We investigate the problem of image retrieval based on visual queries when the latter comprise arbitrary regionsof- interest (ROI) rather than entire images. Our proposal is a compact image descriptor that combines the state-of-the-art in content-based descriptor extraction with a multi-level, Voronoibased spatial partitioning of each dataset image. The proposed multi-level Voronoi-based encoding uses a spatial hierarchical K-means over interest-point locations, and computes a contentbased descriptor over each cell. In order to reduce the matching complexity with minimal or no sacrifice in retrieval performance: (i) we utilize the tree structure of the spatial hierarchical Kmeans to perform a top-to-bottom pruning for local similarity maxima; (ii) we propose a new image similarity score that combines relevant information from all partition levels into a single measure for similarity; (iii) we combine our proposal with a novel and efficient approach for optimal bit allocation within quantized descriptor representations. By deriving both a Voronoi-based VLAD descriptor (termed as Fast-VVLAD) and a Voronoi-based deep convolutional neural network (CNN) descriptor (termed as Fast-VDCNN), we demonstrate that our Voronoi-based framework is agnostic to the descriptor basis, and can easily be slotted into existing frameworks. Via a range of ROI queries in two standard datasets, it is shown that the Voronoibased descriptors achieve comparable or higher mean Average Precision against conventional grid-based spatial search, while offering more than two-fold reduction in complexity. Finally, beyond ROI queries, we show that Voronoi partitioning improves the geometric invariance of compact CNN descriptors, thereby resulting in competitive performance to the current state-of-theart on whole image retrieval
Co-design Hardware and Algorithm for Vector Search
Vector search has emerged as the foundation for large-scale information
retrieval and machine learning systems, with search engines like Google and
Bing processing tens of thousands of queries per second on petabyte-scale
document datasets by evaluating vector similarities between encoded query texts
and web documents. As performance demands for vector search systems surge,
accelerated hardware offers a promising solution in the post-Moore's Law era.
We introduce \textit{FANNS}, an end-to-end and scalable vector search framework
on FPGAs. Given a user-provided recall requirement on a dataset and a hardware
resource budget, \textit{FANNS} automatically co-designs hardware and
algorithm, subsequently generating the corresponding accelerator. The framework
also supports scale-out by incorporating a hardware TCP/IP stack in the
accelerator. \textit{FANNS} attains up to 23.0 and 37.2 speedup
compared to FPGA and CPU baselines, respectively, and demonstrates superior
scalability to GPUs, achieving 5.5 and 7.6 speedup in median
and 95\textsuperscript{th} percentile (P95) latency within an eight-accelerator
configuration. The remarkable performance of \textit{FANNS} lays a robust
groundwork for future FPGA integration in data centers and AI supercomputers.Comment: 11 page
High Performance Geospatial Analysis on Emerging Parallel Architectures
Geographic information systems (GIS) are performing increasingly sophisticated analyses on growing data sets. These analyses demand high performance. At the same time, modern computing platforms increasingly derive their performance from several forms of parallelism. This dissertation explores the available parallelism in several GIS-applied algorithms: viewshed calculation, image feature transform, and feature analysis. It presents implementations of these algorithms that exploit parallel processing to reduce execution time, and analyzes the effectiveness of the implementations in their use of parallel processing
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