text: Deep neural networks (DNNs) have achieved exceptional performances in many tasks, particularly, in supervised classification tasks. However, achievements with supervised classification tasks are based on large datasets with well-separated classes. Typically, real-world applications involve wild datasets that include similar classes; thus, evaluating similarities between classes and understanding relations among classes are important. To address this issue, a similarity metric, ClassSim, based on the misclassification ratios of trained DNNs is proposed herein. We conducted image recognition experiments to demonstrate that the proposed method provides better similarities compared with existing methods and is useful for classification problems. Source code including all experimental results is available at this https URL
  @cite_4: Exploring semantic similarity between concepts in visual domain has a wide range of applications such as natural language processing and multimedia retrieval, which in general requires both a large pool of sample images for each concept and a model to capture its visual characteristics. Instead of relying on high quality and large quantity sample data which is very difficult to obtain, in this paper, a novel method is proposed to achieve improvement in measuring concept similarity by incorporating concept modeling technique into data pruning process. At first, a number of sampling concept models are obtained by sampling a subset from the sample dataset of each concept. Then noisy samples are discarded in terms of their probabilities to the sampling concept models. Experimental results on 31,275 web images of 38 concepts defined in LSCOM indicate that the concept similarity obtained through our proposed approach is more coherent to human cognition. A concept hierarchy tree built from the 38 concepts with their similarity further demonstrates the effectiveness of our proposed method.
  @cite_3: Concept similarity has been intensively researched in the natural language processing domain due to its important role in many applications such as language modeling and information retrieval. There are few studies on measuring concept similarity in visual domain, though concept based multimedia information retrieval has attracted a lot of attentions. In this paper, we present a scalable framework for such a purpose, which is different from traditional approaches to exploring correlation among concepts in image video annotation domain. For each concept, a model based on feature distribution is built using sample images collected from the Internet. And similarity between concepts is measured with the similarity between their models. Hereby, a Gaussian mixture model (GMM) is employed to model each concept and two similarity measurements are investigated. Experimental results on 13,974 images of 16 concepts collected through image search engines have demonstrated that the similarity between concepts is very close to human perception. In addition, the entropy of GMM cluster distributions can be a good indication of selecting concepts for image video annotation.
summary: However, a method to estimate the similarities between classes has been proposed @cite_3 @cite_4 . In that method, images are divided into patches, and features are extracted from each patch using traditional methods, such as RGB color moment. In addition, to compute the distance between classes, we must assume that the images are generated from Gaussian mixture models (GMMs). Note that the number of GMM components must be determined manually relative to the number of target classes. In addition, the distance between classes expresses an inverse relation with similarities; they are not normalized, and their absolute values are meaningless. Here, two distances are involved, i.e., parametric distance (PD), which is the quadratic distance of the means and variances of a GMM, and an approximation of KL divergence. These two methods return similar results. Here, strong assumptions and simplifications were used to treat inter-class similarities realistically.