Semantic Image Retrieval

Semantic Image Retrieval

C.H.C. Leung (Department of Computer Science, Hong Kong Baptist University, Hong Kong) and Yuanxi Li (Hong Kong Baptist University, Hong Kong)
Copyright: © 2015 |Pages: 11
DOI: 10.4018/978-1-4666-5888-2.ch593
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Introduction

The number of Web images is increasing at a rapid rate, and searching them semantically presents a significant challenge. Many raw images are constantly uploaded with little meaningful direct annotation of semantic content, limiting their capacity to be searched and discovered. Unlike in a traditional database, information in an image database is in visual form, which requires more space for storage, is highly unstructured and needs state-of-the-art algorithms to determine its semantic content.

As Web images tend to grow to unwieldy proportions, their retrieval systems must be able to handle multimedia annotation and retrieval on a Web scale with high efficiency and accuracy. With the exception of systems that can identify or detect music, words, faces, irises, smiles, people, pedestrians, or cars, matching is not usually directed toward object semantics. Recent research studies show a large disparity between user needs and technological supply.

Key Terms in this Chapter

Concept-Based Image Indexing: Also variably named as “description-based” or “text-based” image indexing/retrieval, refers to retrieval from text-based indexing of images that may employ keywords, subject headings, captions, or natural language text (Wikipedia, n.d.a).

Semantic Gap: The semantic gap characterizes the difference between two descriptions of an object by different linguistic representations, for instance languages or symbols. In computer science, the concept is relevant whenever ordinary human activities, observations, and tasks are transferred into a computational representation (Wikipedia, n.d.f).

Content-Based Image Retrieval (CBIR): Also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR) is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases ( Datta, Joshi, Li & Wang, 2008 ).

Image Retrieval: An image retrieval system is a computer system for browsing, searching and retrieving images from a large database of digital images. Most traditional and common methods of image retrieval utilize some method of adding metadata such as captioning, keywords, or descriptions to the images so that retrieval can be performed over the annotation words. Manual image annotation is time-consuming, laborious and expensive; to address this, there has been a large amount of research done on automatic image annotation (Wikipedia, n.d.c).

MPEG-7: A multimedia content description standard. It was standardized in ISO/IEC 15938 (Multimedia content description interface). This description will be associated with the content itself, to allow fast and efficient searching for material that is of interest to the user. MPEG-7 is formally called Multimedia Content Description Interface. Thus, it is not a standard which deals with the actual encoding of moving pictures and audio, like MPEG-1, MPEG-2 and MPEG-4. It uses XML to store metadata, and can be attached to timecode in order to tag particular events, or synchronize lyrics to a song (Wikipedia, n.d.d).

Semantics: Is the study of meaning. It focuses on the relation between signifiers, like words, phrases, signs, and symbols, and what they stand for, their denotation (Wikipedia, n.d.g).

Query Expansion (QE): The process of reformulating a seed query to improve retrieval performance in information retrieval operations. In the context of web search engines, query expansion involves evaluating a user's input (what words were typed into the search query area, and sometimes other types of data) and expanding the search query to match additional documents (Wikipedia, n.d.e).

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