  Unlike standard object classification, where the image to be classified
contains one or multiple instances of the same object, indoor scene
classification is quite different since the image consists of multiple distinct
objects. Further, these objects can be of varying sizes and are present across
numerous spatial locations in different layouts. For automatic indoor scene
categorization, large scale spatial layout deformations and scale variations
are therefore two major challenges and the design of rich feature descriptors
which are robust to these challenges is still an open problem. This paper
introduces a new learnable feature descriptor called "spatial layout and scale
invariant convolutional activations" to deal with these challenges. For this
purpose, a new Convolutional Neural Network architecture is designed which
incorporates a novel 'Spatially Unstructured' layer to introduce robustness
against spatial layout deformations. To achieve scale invariance, we present a
pyramidal image representation. For feasible training of the proposed network
for images of indoor scenes, the paper proposes a new methodology which
efficiently adapts a trained network model (on a large scale data) for our task
with only a limited amount of available training data. Compared with existing
state of the art, the proposed approach achieves a relative performance
improvement of 3.2%, 3.8%, 7.0%, 11.9% and 2.1% on MIT-67, Scene-15, Sports-8,
Graz-02 and NYU datasets respectively.
