29 research outputs found
Parametric Local Metric Learning for Nearest Neighbor Classification
We study the problem of learning local metrics for nearest neighbor
classification. Most previous works on local metric learning learn a number of
local unrelated metrics. While this "independence" approach delivers an
increased flexibility its downside is the considerable risk of overfitting. We
present a new parametric local metric learning method in which we learn a
smooth metric matrix function over the data manifold. Using an approximation
error bound of the metric matrix function we learn local metrics as linear
combinations of basis metrics defined on anchor points over different regions
of the instance space. We constrain the metric matrix function by imposing on
the linear combinations manifold regularization which makes the learned metric
matrix function vary smoothly along the geodesics of the data manifold. Our
metric learning method has excellent performance both in terms of predictive
power and scalability. We experimented with several large-scale classification
problems, tens of thousands of instances, and compared it with several state of
the art metric learning methods, both global and local, as well as to SVM with
automatic kernel selection, all of which it outperforms in a significant
manner
Learning Fine-grained Image Similarity with Deep Ranking
Learning fine-grained image similarity is a challenging task. It needs to
capture between-class and within-class image differences. This paper proposes a
deep ranking model that employs deep learning techniques to learn similarity
metric directly from images.It has higher learning capability than models based
on hand-crafted features. A novel multiscale network structure has been
developed to describe the images effectively. An efficient triplet sampling
algorithm is proposed to learn the model with distributed asynchronized
stochastic gradient. Extensive experiments show that the proposed algorithm
outperforms models based on hand-crafted visual features and deep
classification models.Comment: CVPR 201
Image retrieval based on bag of images
Conventional relevance feedback schemes may not be suitable to all practical applications of content-based image retrieval (CBIR), since most ordinary users would like to complete their search in a single interaction, especially on the web search. In this paper, we explore a new approach to improve the retrieval performance based on a new concept, bag of images, rather than relevance feedback. We consider that image collection comprises of image bags instead of independent individual images. Each image bag includes some relevant images with the same perceptual meaning. A theoretical case study demonstrates that image retrieval can benefit from the new concept. A number of experimental results show that the CBIR scheme based on bag of images can improve the retrieval performance dramatically.<br /