5,756 research outputs found
An Efficient Index for Visual Search in Appearance-based SLAM
Vector-quantization can be a computationally expensive step in visual
bag-of-words (BoW) search when the vocabulary is large. A BoW-based appearance
SLAM needs to tackle this problem for an efficient real-time operation. We
propose an effective method to speed up the vector-quantization process in
BoW-based visual SLAM. We employ a graph-based nearest neighbor search (GNNS)
algorithm to this aim, and experimentally show that it can outperform the
state-of-the-art. The graph-based search structure used in GNNS can efficiently
be integrated into the BoW model and the SLAM framework. The graph-based index,
which is a k-NN graph, is built over the vocabulary words and can be extracted
from the BoW's vocabulary construction procedure, by adding one iteration to
the k-means clustering, which adds small extra cost. Moreover, exploiting the
fact that images acquired for appearance-based SLAM are sequential, GNNS search
can be initiated judiciously which helps increase the speedup of the
quantization process considerably
Multiple Instance Learning: A Survey of Problem Characteristics and Applications
Multiple instance learning (MIL) is a form of weakly supervised learning
where training instances are arranged in sets, called bags, and a label is
provided for the entire bag. This formulation is gaining interest because it
naturally fits various problems and allows to leverage weakly labeled data.
Consequently, it has been used in diverse application fields such as computer
vision and document classification. However, learning from bags raises
important challenges that are unique to MIL. This paper provides a
comprehensive survey of the characteristics which define and differentiate the
types of MIL problems. Until now, these problem characteristics have not been
formally identified and described. As a result, the variations in performance
of MIL algorithms from one data set to another are difficult to explain. In
this paper, MIL problem characteristics are grouped into four broad categories:
the composition of the bags, the types of data distribution, the ambiguity of
instance labels, and the task to be performed. Methods specialized to address
each category are reviewed. Then, the extent to which these characteristics
manifest themselves in key MIL application areas are described. Finally,
experiments are conducted to compare the performance of 16 state-of-the-art MIL
methods on selected problem characteristics. This paper provides insight on how
the problem characteristics affect MIL algorithms, recommendations for future
benchmarking and promising avenues for research
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