  Image representation and classification are two fundamental tasks towards
multimedia content retrieval and understanding. The idea that shape and texture
information (e.g. edge or orientation) are the key features for visual
representation is ingrained and dominated in current multimedia and computer
vision communities. A number of low-level features have been proposed by
computing local gradients (e.g. SIFT, LBP and HOG), and have achieved great
successes on numerous multimedia applications. In this paper, we present a
simple yet efficient local descriptor for image classification, referred as
Local Color Contrastive Descriptor (LCCD), by leveraging the neural mechanisms
of color contrast. The idea originates from the observation in neural science
that color and shape information are linked inextricably in visual cortical
processing. The color contrast yields key information for visual color
perception and provides strong linkage between color and shape. We propose a
novel contrastive mechanism to compute the color contrast in both spatial
location and multiple channels. The color contrast is computed by measuring
\emph{f}-divergence between the color distributions of two regions. Our
descriptor enriches local image representation with both color and contrast
information. We verified experimentally that it can compensate strongly for the
shape based descriptor (e.g. SIFT), while keeping computationally simple.
Extensive experimental results on image classification show that our descriptor
improves the performance of SIFT substantially by combinations, and achieves
the state-of-the-art performance on three challenging benchmark datasets. It
improves recent Deep Learning model (DeCAF) [1] largely from the accuracy of
40.94% to 49.68% in the large scale SUN397 database. Codes for the LCCD will be
available.
