Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual Features
This addresses fine-grained image analysis for computer vision applications, representing an incremental improvement through multimodal fusion.
The paper tackles fine-grained image classification and retrieval by combining visual features with textual information extracted from images using PHOC descriptors and Fisher Vector Encoding. The approach achieves state-of-the-art results on both tasks.
Text contained in an image carries high-level semantics that can be exploited to achieve richer image understanding. In particular, the mere presence of text provides strong guiding content that should be employed to tackle a diversity of computer vision tasks such as image retrieval, fine-grained classification, and visual question answering. In this paper, we address the problem of fine-grained classification and image retrieval by leveraging textual information along with visual cues to comprehend the existing intrinsic relation between the two modalities. The novelty of the proposed model consists of the usage of a PHOC descriptor to construct a bag of textual words along with a Fisher Vector Encoding that captures the morphology of text. This approach provides a stronger multimodal representation for this task and as our experiments demonstrate, it achieves state-of-the-art results on two different tasks, fine-grained classification and image retrieval.