Abstract:
Bag-of-Features (BoF) approach have been
successfully applied to visual object classification tasks. Recently,
convolutional neural networks (CNNs) demonstrated excellent
performance on object classification problems. In this paper we
propose to construct a new feature set by processing CNN
activations from convolutional layers fused with the traditional
BoF representation for efficient object classification using SVMs.
The dimension of convolutional features were reduced using
PCA technique and the bag-of-features representation was
reduced by tailoring the visual codebook using a statistical
codeword selection method, in order to obtain a compact
representation of the new feature set which achieves increased
classification rate while requiring less storage. The proposed
framework, based on the new features, outperforms other
state-of-the-art approaches that have been evaluated on
benchmark datasets: Xerox7, UIUC Texture, and Caltech-101.
