2 research outputs found
Feature Learning by Multidimensional Scaling and its Applications in Object Recognition
We present the MDS feature learning framework, in which multidimensional
scaling (MDS) is applied on high-level pairwise image distances to learn
fixed-length vector representations of images. The aspects of the images that
are captured by the learned features, which we call MDS features, completely
depend on what kind of image distance measurement is employed. With properly
selected semantics-sensitive image distances, the MDS features provide rich
semantic information about the images that is not captured by other feature
extraction techniques. In our work, we introduce the iterated
Levenberg-Marquardt algorithm for solving MDS, and study the MDS feature
learning with IMage Euclidean Distance (IMED) and Spatial Pyramid Matching
(SPM) distance. We present experiments on both synthetic data and real images
--- the publicly accessible UIUC car image dataset. The MDS features based on
SPM distance achieve exceptional performance for the car recognition task.Comment: To appear in SIBGRAPI 201
Marine Animal Classification with Correntropy Loss Based Multi-view Learning
To analyze marine animals behavior, seasonal distribution and abundance,
digital imagery can be acquired by visual or Lidar camera. Depending on the
quantity and properties of acquired imagery, the animals are characterized as
either features (shape, color, texture, etc.), or dissimilarity matrices
derived from different shape analysis methods (shape context, internal distance
shape context, etc.). For both cases, multi-view learning is critical in
integrating more than one set of feature or dissimilarity matrix for higher
classification accuracy. This paper adopts correntropy loss as cost function in
multi-view learning, which has favorable statistical properties for rejecting
noise. For the case of features, the correntropy loss-based multi-view learning
and its entrywise variation are developed based on the multi-view intact space
learning algorithm. For the case of dissimilarity matrices, the robust
Euclidean embedding algorithm is extended to its multi-view form with the
correntropy loss function. Results from simulated data and real-world marine
animal imagery show that the proposed algorithms can effectively enhance
classification rate, as well as suppress noise under different noise
conditions