Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Text in natural images typically adds meaning to an object or scene. In particular, text specifies which business places serve drinks (e.g., cafe, teahouse) or food (e.g., restaurant, pizzeria), and what kind of service is provided (e.g., massage, repair). The mere presence of text, its words, and meaning are closely related to the semantics of the object or scene.  exploits textual contents in images for fine-grained business place classification and logo retrieval. There are four main contributions. First,  show that the textual cues extracted by the proposed method are effective for the two tasks. Combining the proposed textual and visual cues outperforms visual only classification and retrieval by a large margin. Second, to extract the textual cues, a generic and fully unsupervised word box proposal method is introduced. The method reaches state-of-the-art word detection recall with a limited number of proposals. Third, contrary to what is widely acknowledged in text detection literature,  demonstrate that high recall in word detection is more important than high f-score at least for both tasks considered in this work. Last, this paper provides a large annotated text detection dataset with 10 K images and 27 601 word boxes.
['Sezer Karaoglu', 'Ran Tao', 'Theo Gevers', 'Arnold W. M. Smeulders']
I am writing a research paper on a idea Words Matter: Scene Text for Image Classification and Retrieval
[{"from": "human", "value": "I am writing a research paper on a idea Words Matter: Scene Text for Image Classification and Retrieval"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Text in natural images typically adds meaning to an object or scene. In particular, text specifies which business places serve drinks (e.g., cafe, teahouse) or food (e.g., restaurant, pizzeria), and what kind of service is provided (e.g., massage, repair). The mere presence of text, its words, and meaning are closely related to the semantics of the object or scene.  exploits textual contents in images for fine-grained business place classification and logo retrieval. There are four main contributions. First,  show that the textual cues extracted by the proposed method are effective for the two tasks. Combining the proposed textual and visual cues outperforms visual only classification and retrieval by a large margin. Second, to extract the textual cues, a generic and fully unsupervised word box proposal method is introduced. The method reaches state-of-the-art word detection recall with a limited number of proposals. Third, contrary to what is widely acknowledged in text detection literature,  demonstrate that high recall in word detection is more important than high f-score at least for both tasks considered in this work. Last, this paper provides a large annotated text detection dataset with 10\u00a0K images and 27\u2009601 word boxes."}]