4,162 research outputs found
Image Reconstruction from Bag-of-Visual-Words
The objective of this work is to reconstruct an original image from
Bag-of-Visual-Words (BoVW). Image reconstruction from features can be a means
of identifying the characteristics of features. Additionally, it enables us to
generate novel images via features. Although BoVW is the de facto standard
feature for image recognition and retrieval, successful image reconstruction
from BoVW has not been reported yet. What complicates this task is that BoVW
lacks the spatial information for including visual words. As described in this
paper, to estimate an original arrangement, we propose an evaluation function
that incorporates the naturalness of local adjacency and the global position,
with a method to obtain related parameters using an external image database. To
evaluate the performance of our method, we reconstruct images of objects of 101
kinds. Additionally, we apply our method to analyze object classifiers and to
generate novel images via BoVW
Fast Low-rank Representation based Spatial Pyramid Matching for Image Classification
Spatial Pyramid Matching (SPM) and its variants have achieved a lot of
success in image classification. The main difference among them is their
encoding schemes. For example, ScSPM incorporates Sparse Code (SC) instead of
Vector Quantization (VQ) into the framework of SPM. Although the methods
achieve a higher recognition rate than the traditional SPM, they consume more
time to encode the local descriptors extracted from the image. In this paper,
we propose using Low Rank Representation (LRR) to encode the descriptors under
the framework of SPM. Different from SC, LRR considers the group effect among
data points instead of sparsity. Benefiting from this property, the proposed
method (i.e., LrrSPM) can offer a better performance. To further improve the
generalizability and robustness, we reformulate the rank-minimization problem
as a truncated projection problem. Extensive experimental studies show that
LrrSPM is more efficient than its counterparts (e.g., ScSPM) while achieving
competitive recognition rates on nine image data sets.Comment: accepted into knowledge based systems, 201
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