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Mobile Visual Search with Text and Image Features -- Sam S. Tsai

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Published on Dec 21, 2013

Mobile phones, equipped with powerful processors, high resolution cameras, and sharp color displays, enable a new class of visual search applications. While the bag of image features based matching approach works nicely for most visual search applications, its success on retrieving images with text have been limited. Text in images are noticeable and descriptive, and yet its repetitive structures cause problems for retrieval pipelines based on image features. One way to get by this problem is to perform character recognition. However, wide variations observed in mobile phone taken images make it difficult for generic character recognition engines to recognize the characters reliably.

In this talk, we will introduce a new image retrieval framework that uses visual text information for visual search which aims to solve the problems mentioned. We will first introduce how we locate the visual text in images with background clutter. We describe a bottom-up text detection algorithm which extracts maximally stable extremal regions as character candidates. From these character candidates, text lines and words are formed. Then, we present a Word Histogram of Oriented Gradients (Word-HOG) descriptor that is generated from the detected word patches. The Word-HOG descriptor is shown to have better word matching performance than state-of-the-art algorithms and character recognition engines. Furthermore, it can be efficiently compressed for data transmission. Finally, we describe the image retrieval framework that uses visual text. We show that the framework will work for scenarios with different types of visual text images. We also explain the database reduction scheme based on random sampling that enables us to perform large scale image retrieval.

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