Content-based image retrieval

About: Content-based image retrieval is a research topic. Over the lifetime, 6916 publications have been published within this topic receiving 150696 citations. The topic is also known as: CBIR.

Keyword and image-based retrieval of mathematical expressions

Abstract: Two new methods for retrieving mathematical expressions using conventional keyword search and expression images are presented. An expression-level TF-IDF (term frequency-inverse document frequency) approach is used for keyword search, where queries and indexed expressions are represented by keywords taken from LATEX strings. TF-IDF is computed at the level of individual expressions rather than documents to increase the precision of matching. The second retrieval technique is a form of Content-Based Image Retrieval (CBIR). Expressions are segmented into connected components, and then components in the query expression and each expression in the collection are matched using contour and density features, aspect ratios, and relative positions. In an experiment using ten randomly sampled queries from a corpus of over 22,000 expressions, precision-at-k (k = 20) for the keyword-based approach was higher (keyword: μ = 84.0, σ = 19.0, imagebased: μ = 32.0, σ = 30.7), but for a few of the queries better results were obtained using a combination of the two techniques.

Robust Image Retrieval using Multiview Scalable Vocabulary Trees

Abstract: Content-based image retrieval using a Scalable Vocabulary Tree (SVT) built from local scale-invariant features is an effective method of fast search through a database. An SVT built from fronto-parallel database images, however, is ineffective at classifying query images that suffer from perspective distortion. In this paper, we propose an efficient server-side extension of the single-view SVT to a set of multiview SVTs that may be simultaneously employed for image classification. Our solution results in significantly better retrieval performance when perspective distortion is present. We also develop an analysis of how perspective increases the distance between matching query-database feature descriptors.

Comparison of Curvelet and Wavelet Texture Features for Content Based Image Retrieval

Abstract: Texture feature plays a vital role in content based Image retrieval (CBIR). Wavelet texture feature modeled by generalized Gaussian density (GGD) [1] performs better than discrete wavelet texture feature. Curve let texture feature was proposed in [2]. In this paper, we compute a new texture feature by applying the generalized Gaussian density to the distribution of curve let coefficients which we call curve let GGD texture feature. The purpose of this paper is to investigate curve let GGD texture feature and compare its retrieval performance with that of curve let, wavelet and wavelet GGD texture features. Experimental results show that both curve let and curve let GGD features perform significantly better than wavelet and wavelet GGD texture features. Among the two types of curve let based features, curve let feature shows better performance in CBIR than curve let GGD texture feature. The findings are discussed in the paper.

Performance evaluation of image retrieval using energy compaction and imagetiling over DCT row mean and DCT column mean

Abstract: Content-based image retrieval (CBIR) is a technique to retrieve images on the basis of image specific features such as colour [5, 17, 19], texture [11, 14, 16, 23] and shape [2, 4]. CBIR operates on a totally different principle from keyword indexing. Initially, features are computed for both stored and query images, and used to identify images most closely matching the query. The need to find a desired image from a collection is shared by many professional groups, including journalists, design engineers [24] and art historians. While the requirements of image users can vary considerably, it can be useful to characterize image queries into three levels of abstraction: primitive features such as colour or shape, logical features such as the identity of objects shown and abstract attributes such as the texture of image depicted [12, 15, 18]. Users needing to retrieve images from a collection come from a variety of domains, including crime prevention [13, 20], medicine, architecture [6, 9, 10, 21, 22], fashion and publishing. Many techniques have been proposed for content based image retrieval, but still there is thirst for better performance [1, 3] and faster retrieval [1, 7, 8].