We present image spot queries as an alternative for content-based image retrieval. Through image spots (i.e., selective parts of images) users can better indicate which parts of the image are relevant for their query. Compared to traditional approaches, we allow users to search image databases for local image (spatial, color and color transition) characteristics rather than global features.

Content-based image retrieval (CBIR) makes use of image features, such as color and texture, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. This chapter introduces a content-based approach to medical image retrieval. Fundamentals of the key components of content-based image retrieval systems are introduced ...

Article history: Received 16 November 2007 Received in revised form 4 May 2008 Accepted 23 June 2008

We propose a novel query-dependent feature aggregation (QDFA) method for medical image retrieval. The QDFA method can learn an optimal feature aggregation function for a multi-example query, which takes into account multiple features and multiple examples with different importance. The experiments demonstrate that the QDFA method outperforms three other feature aggregation methods. key words: C...

Abstract This article considers the problem of how to formulate a framework for the study of the nearness of tolerance rough sets (TRS). The solution to the problem stems from recent work on near sets and approach spaces as well as from the realisation that disjoint TRSs can be viewed in the context of approach merotopic spaces. A set of TRSs equipped with a distance function satisfying certain...

In content-based image retrieval (CBIR), the user usually poses several labelled images and then the system attempts to retrieve all the images relevant to the target concept defined by these labelled images. It may be helpful if the system can return relevant images where the regions of interest (ROI) are explicitly located. In this paper, this task is accomplished with the help of multi-insta...

In this paper we look at an image retrieval scenario that we call mental image category search, where the user has an image class in his or her mind and wishes to retrieve several such images from a database, without the benefit of an example. We propose a content-based image retrieval system based on Bayesian inference. The approach is evaluated by performing searches for categories of image t...

In this paper we propose to employ human visual attention models for content based image retrieval. This approach is called query by saliency content retrieval (QSCR) and considers visual saliency at both local and global image levels. Each image, from a given database, is segmented and specific features are evaluated locally for each of its regions. The global saliency is evaluated based on ed...

Biorthonormal M-band wavelet transform is used to decompose the image into sub-bands for constructing the feature database in content-based image retrieval of 1856 Brodatz texture images. Texture features are obtained by computing the measure of energy, standard deviation and its combination on each band. Results are far superior and impressive than conventional two-band wavelet decomposition. ...