Experiments of Image Classification Using Dissimilarity Spaces Built with Siamese Networks

Abstract

Traditionally, classifiers are trained to predict patterns within a feature space. The image classification system presented here trains classifiers to predict patterns within a vector space by combining the dissimilarity spaces generated by a large set of Siamese Neural Networks (SNNs). A set of centroids from the patterns in the training data sets is calculated with supervised k-means clustering. The centroids are used to generate the dissimilarity space via the Siamese networks. The vector space descriptors are extracted by projecting patterns onto the similarity spaces, and SVMs classify an image by its dissimilarity vector. The versatility of the proposed approach in image classification is demonstrated by evaluating the system on different types of images across two domains: two medical data sets and two animal audio data sets with vocalizations represented as images (spectrograms). Results show that the proposed system's performance competes competitively against the best-performing methods in the literature, obtaining state-of-the-art performance on one of the medical data sets, and does so without ad-hoc optimization of the clustering methods on the tested data sets.

Closing the Performance Gap between Siamese Networks for Dissimilarity Image Classification and Convolutional Neural Networks

“…The basic system can be described as follows. The inputs into the system, as in [12][13][14], are the original images and HASC descriptors [19], extracted to produce a new processed image. If the original image is in color, Hasc is applied separately on each band; if it is grey level, the Hasc image is replicated three times to build an image with three bands.…”

“…In the testing stage, an unknown pattern is projected onto the dissimilarity space that was learned by the SNN, which generates the feature vector that is then fed into the trained SVM for a decision. The SNN, as illustrated in Figure 2, combines two identical deep learners whose outputs are subtracted, which produces a feature vector (the absolute value of the difference) that is passed to a sigmoid and a loss function as in [12][13][14]. Unlike [12-14], which used binary cross entropy, two different loss functions are tested here (binary cross entropy and triplet loss function), and the CNN subnets are optimized with Adam and some Adam variants.…”

“…Though some variations are indicated in Figures 1 and 2, they only show the output of one SNN fed into one SVM. In [12][13][14] and this work, many SNNs and SVMs are trained, tested, and combined. Eight CNN topologies form the backbone of the SNNs.…”

“…Eight CNN topologies form the backbone of the SNNs. These are the identical topologies described in [14] (for the reader's convenience, the table in [14] that details the topologies is reprinted in the Appendix). ).…”

“…Though the two terms, similarity and dissimilarity are rarely disambiguated in the literature, classification based on the notion of dissimilarity is an idea first proposed in [1], where the focus was on comparing differences between samples belonging to different classes. Dissimilarity classification can be tackled by using either dissimilarity vectors, as in [2][3][4][5][6], or dissimilarity spaces, as in [7][8][9][10][11][12][13][14]. In the former case, two samples are considered positive if they belong to the same class and negative if they belong to separate classes.…”