Author:
Avi Bleiweiss
Affiliation:
Intel Corporation, United States
Keyword(s):
Dimensionality Reduction, Image Decomposition, Disjoint Set, Histogram of Oriented Gradient.
Related
Ontology
Subjects/Areas/Topics:
Clustering
;
Feature Selection and Extraction
;
Pattern Recognition
;
Similarity and Distance Learning
;
Sparsity
;
Theory and Methods
Abstract:
A common objective in multi class, image analysis is to reduce the dimensionality of input data, and capture the most discriminant features in the projected space. In this work, we investigate a system that first finds clusters of similar points in feature space, using a nearest neighbor, graph based decomposition algorithm. This process transforms the original image data on to a subspace of identical dimensionality, but at a much flatter, color gamut. The intermediate representation of the segmented image, follows an effective, local descriptor operator that yields a marked compact feature vector, compared to the one obtained from a descriptor, immediately succeeding the native image. For evaluation, we study a generalized, multi resolution representation of decomposed images, parameterized by a broad range of a decreasing number of clusters. We conduct experiments on both non and correlated image sets, expressed in raw feature vectors of one million elements each, and demonstrate
robust accuracy in applying our features to a linear SVM classifier. Compared to state-of-the-art systems of identical goals, our method shows increased dimensionality reduction, at a consistent feature matching performance.
(More)