10,496 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
Prototypicality effects in global semantic description of objects
In this paper, we introduce a novel approach for semantic description of
object features based on the prototypicality effects of the Prototype Theory.
Our prototype-based description model encodes and stores the semantic meaning
of an object, while describing its features using the semantic prototype
computed by CNN-classifications models. Our method uses semantic prototypes to
create discriminative descriptor signatures that describe an object
highlighting its most distinctive features within the category. Our experiments
show that: i) our descriptor preserves the semantic information used by the
CNN-models in classification tasks; ii) our distance metric can be used as the
object's typicality score; iii) our descriptor signatures are semantically
interpretable and enables the simulation of the prototypical organization of
objects within a category.Comment: Paper accepted in IEEE Winter Conference on Applications of Computer
Vision 2019 (WACV2019). Content: 10 pages (8 + 2 reference) with 7 figure
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