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Text contained in an image carries high-level semantics that can be exploitedto achieve richer image understanding. In particular, the mere presence of textprovides strong guiding content that should be employed to tackle a diversityof computer vision tasks such as image retrieval, fine-grained classification,and visual question answering. In this paper, we address the problem offine-grained classification and image retrieval by leveraging textualinformation along with visual cues to comprehend the existing intrinsicrelation between the two modalities. The novelty of the proposed model consistsof the usage of a PHOC descriptor to construct a bag of textual words alongwith a Fisher Vector Encoding that captures the morphology of text. Thisapproach provides a stronger multimodal representation for this task and as ourexperiments demonstrate, it achieves state-of-the-art results on two differenttasks, fine-grained classification and image retrieval.

Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual Features