Knowledge transfer, zero-shot learning and semantic image retrieval are methods that aim at improving accuracy by utilizing semantic information, e.g. from WordNet. It is assumed that this information can augment or replace missing visual data in the form of labeled training images because semantic similarity somewhat aligns with visual similarity. This assumption may seem trivial, but is crucial for the application of such semantic methods. Any violation can cause mispredictions. Thus, it is important to examine the visual-semantic relationship for a certain target problem. In this paper, we use five different semantic and visual similarity measures each to thoroughly analyze the relationship without relying too much on any single definition. We postulate and verify three highly consequential hypotheses on the relationship. Our results show that it indeed exists and that WordNet semantic similarity carries more information about visual similarity than just the knowledge of "different classes look different". They suggest that classification is not the ideal application for semantic methods and that wrong semantic information is much worse than none.
Content-based image retrieval is an area that profits significantly from semantic information, especially when such systems are judged by a human ranking baseline. Vogel and Schiele propose an image representation in @cite_14 that describes an image's semantics locally that is then used to rank images by semantic similarity the query. An approach specifically using taxonomic information is presented by Barz al in @cite_8 , where an embedding space is constructed such that the dot product of label pairs matches a semantic similarity measure. They show that this label representation improves both image retrieval as well as image classification.
@cite_14 @cite_8
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In this paper, we present a novel image representation that renders it possible to access natural scenes by local semantic description. Our work is motivated by the continuing effort in content-based image retrieval to extract and to model the semantic content of images. The basic idea of the semantic modeling is to classify local image regions into semantic concept classes such as water, rocks, or foliage. Images are represented through the frequency of occurrence of these local concepts. Through extensive experiments, we demonstrate that the image representation is well suited for modeling the semantic content of heterogenous scene categories, and thus for categorization and retrieval. The image representation also allows us to rank natural scenes according to their semantic similarity relative to certain scene categories. Based on human ranking data, we learn a perceptually plausible distance measure that leads to a high correlation between the human and the automatically obtained typicality ranking. This result is especially valuable for content-based image retrieval where the goal is to present retrieval results in descending semantic similarity from the query. Deep neural networks trained for classification have been found to learn powerful image representations, which are also often used for other tasks such as comparing images w.r.t. their visual similarity. However, visual similarity does not imply semantic similarity. In order to learn semantically discriminative features, we propose to map images onto class embeddings whose pair-wise dot products correspond to a measure of semantic similarity between classes. Such an embedding does not only improve image retrieval results, but could also facilitate integrating semantics for other tasks, e.g., novelty detection or few-shot learning. We introduce a deterministic algorithm for computing the class centroids directly based on prior world-knowledge encoded in a hierarchy of classes such as WordNet. Experiments on CIFAR-100, NABirds, and ImageNet show that our learned semantic image embeddings improve the semantic consistency of image retrieval results by a large margin.