Semantic Image Clustering with Global Average Pooled Deep Convolutional Autoencoder
Morarjee Kolla · Helix · 2018
Deep Clustering learns feature representations in embedded space suitable for clustering.In Deep Convolutional Embedded Clustering (DCEC) algorithm, the last convolution layer feature map of encoder is used to build the embedded space.This considers spatial information retains in the last convolution layer of encoder, which unable to identify the discriminative parts of the image.To address this issue, we propose a solution using Global Average Pooling (GAP) of the last convolution layer feature maps in the encoder.This will encourage the network to identify all discriminative regions and an extent of an object to formulate semantic image clusters (SIC).Our experimental results prove the efficiency of proposed Global Average Pooled Deep Convolutional Embedded Clustering (GAPDCEC) for simultaneous feature learning and clustering.