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.

Content-based image retrieval: a comparison between query by example and image browsing map approaches

Abstract: Efficient and effective retrieval techniques of images are desired because of the explosive growth of digital images. Content-based image retrieval is a promising approach because of its automatic indexing and retrieval based on their semantic features and visual appearance. The similarity of images depends on the feature representation and feature dissimilarity function. However, users have difficulties in representing their information needs in queries to content-based image retrieval systems. In this paper, we investigate two approaches, query by example and image browsing map. Activities to support the information seeking behavior are analyzed. The performance of these approaches is measured by a user evaluation. It is found that the image browsing map provides more functionalities and capabilities to support the features of information seeking behavior and produces better performance in searching images.

Microcalcification Classification Assisted by Content-Based Image Retrieval for Breast Cancer Diagnosis

Abstract: In this paper we propose a microcalcification classification scheme, assisted by content-based mammogram retrieval, for breast cancer diagnosis. We recently developed a machine learning approach for mammogram retrieval where the similarity measure between two lesion mammograms is modeled after expert observers. In this work we investigate how to use retrieved similar cases as references to improve the performance of a numerical classifier. Our rationale is that by adap-tively incorporating local proximity information into a classifier, it can help improve its classification accuracy, thereby leading to an improved "second opinion" to radiologists. Our experimental results on a mammogram database demonstrate that the proposed retrieval-driven approach with an adaptive support vector machine (SVM) could improve the classification performance from 0.78 to 0.82 in terms of the area under the ROC curve.

Fast content-based image retrieval using quasi-Gabor filter and reduction of image feature dimension

Abstract: This paper introduces a new approach to content based image retrieval by texture. There are three problems to solve: high computational time, handling high dimension data, and comparing images consistent with human perception. To decrease the computational, time, we present a new strategy to extract an image feature with high retrieval accuracy. We also propose how to reduce the image feature dimension using the reward-punishment algorithm, so any robust indexing methods can be used. By weighting the extracted image features, a system may perceive the image consistently with human perception.

Creating a large-scale content-based airphoto image digital library

Abstract: We describe a content-based image retrieval digital library that supports geographical image retrieval over a testbed of 800 aerial photographs, each 25 megabytes in size In addition, this paper also introduces a methodology to evaluate the performance of the algorithms in the prototype system There are two major contributions: we suggest an approach that incorporates various image processing techniques including Gabor filters, image enhancement and image compression, as well as information analysis techniques such as the self-organizing map (SOM) into an effective large-scale geographical image retrieval system We present two experiments that evaluate the performance of the Gabor-filter-extracted features along with the corresponding similarity measure against that of human perception, addressing the lack of studies in assessing the consistency between an image representation algorithm or an image categorization method and human mental model

Multi Feature Content Based Image Retrieval

Abstract: There are numbers of methods prevailing for Image Mining Techniques This Paper includes the features of four techniques I,e Color Histogram, Color moment, Texture, and Edge Histogram Descriptor The nature of the Image is basically based on the Human Perception of the Image The Machine interpretation of the Image is based on the Contours and surfaces of the Images The study of the Image Mining is a very challenging task because it involves the Pattern Recognition which is a very important tool for the Machine Vision system A combination of four feature extraction methods namely color Histogram, Color Moment, texture, and Edge Histogram Descriptor There is a provision to add new features in future for better retrieval efficiency In this paper the combination of the four techniques are used and the Euclidian distances are calculated of the every features are added and the averages are made The user interface is provided by the Mat lab The image properties analyzed in this work are by using computer vision and image processing algorithms For color the histogram of images are computed, for texture co occurrence matrix based entropy, energy, etc, are calculated and for edge density it is Edge Histogram Descriptor (EHD) that is found For retrieval of images, the averages of the four techniques are made and the resultant Image is retrieved Keywords-component; Content Based Image Retrieval (CBIR), Edge Histogram Descriptor (EHD),Color moment ,textures, Color Histogram