6,744 research outputs found
No Spare Parts: Sharing Part Detectors for Image Categorization
This work aims for image categorization using a representation of distinctive
parts. Different from existing part-based work, we argue that parts are
naturally shared between image categories and should be modeled as such. We
motivate our approach with a quantitative and qualitative analysis by
backtracking where selected parts come from. Our analysis shows that in
addition to the category parts defining the class, the parts coming from the
background context and parts from other image categories improve categorization
performance. Part selection should not be done separately for each category,
but instead be shared and optimized over all categories. To incorporate part
sharing between categories, we present an algorithm based on AdaBoost to
jointly optimize part sharing and selection, as well as fusion with the global
image representation. We achieve results competitive to the state-of-the-art on
object, scene, and action categories, further improving over deep convolutional
neural networks
Attribute-Graph: A Graph based approach to Image Ranking
We propose a novel image representation, termed Attribute-Graph, to rank
images by their semantic similarity to a given query image. An Attribute-Graph
is an undirected fully connected graph, incorporating both local and global
image characteristics. The graph nodes characterise objects as well as the
overall scene context using mid-level semantic attributes, while the edges
capture the object topology. We demonstrate the effectiveness of
Attribute-Graphs by applying them to the problem of image ranking. We benchmark
the performance of our algorithm on the 'rPascal' and 'rImageNet' datasets,
which we have created in order to evaluate the ranking performance on complex
queries containing multiple objects. Our experimental evaluation shows that
modelling images as Attribute-Graphs results in improved ranking performance
over existing techniques.Comment: In IEEE International Conference on Computer Vision (ICCV) 201
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