The role of visual semantic similarity in image segmentation


The role of visual semantic similarity in image segmentation – The objective of this paper is to study the influence of the visual similarity across images in how images are classified. The purpose of this work is to determine whether visual similarity between images has a similar or opposite effect or whether it is a function of each image’s class and which images would not be classified as similar. For both categories, it is important to estimate the effect of visual similarity across image images. We propose a novel method that estimates the visual similarity using a convolutional neural network (CNN) and train a discriminator to identify object category in each image. The CNN model is trained on RGB images whose categories were not labeled, and the discriminator performs a multi-label classification using multi-label prediction strategy. Experiments on ImageNet30 and CNN-76 are conducted on benchmark images and are compared with several state-of-the-art CNN models. The results indicate that visual similarity varies between CNN and CNN-76.

We present a novel system for classification of neural networks. The system is based on a novel CNN architecture, called CNN + Multi-Network (CNN-MMS) for visual classification. The CNN-MMS architecture is based on a fully convolutional network, and therefore the CNN-MMS architecture is only an initial step towards learning the classification. We train CNN-MMS using two CNN-MMS architectures: A new CNN-MA (CNN-MA2) architecture, built upon a unified model and a CNN-MMS architecture. We compare the performance of CNN-MA2 and CNN-MMS using two public datasets, showing that CNN-MA2 achieves better classification performance than CNN-MMS. Besides, CNN-MMS achieves the best classification performance reported on the MNIST dataset.

A novel fuzzy clustering technique based on minimum parabolic filtering and prediction by distributional evolution

Convolutional Sparse Coding for Unsupervised Image Segmentation

The role of visual semantic similarity in image segmentation

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  • Sparse Clustering via Convex Optimization

    On the Use of Neural Networks for Active LearningWe present a novel system for classification of neural networks. The system is based on a novel CNN architecture, called CNN + Multi-Network (CNN-MMS) for visual classification. The CNN-MMS architecture is based on a fully convolutional network, and therefore the CNN-MMS architecture is only an initial step towards learning the classification. We train CNN-MMS using two CNN-MMS architectures: A new CNN-MA (CNN-MA2) architecture, built upon a unified model and a CNN-MMS architecture. We compare the performance of CNN-MA2 and CNN-MMS using two public datasets, showing that CNN-MA2 achieves better classification performance than CNN-MMS. Besides, CNN-MMS achieves the best classification performance reported on the MNIST dataset.


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