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.

Characteristics of weighted feature vector in content-based image retrieval applications

Abstract: Color and texture feature vectors of an image are always considered to be an important attribute in content-based image retrieval system. Both of these feature vectors of an image can be combined for the performance enhancement of the content-based image retrieval system. One of the standard ways of extracting color feature from an image is to generate a color histogram. Using Haar wavelet or Daubechies' wavelet the texture feature of an image can be extracted. These two feature vectors and the feature vectors in the database are normalized so that the value of a bin is always between [0,1]. During retrieval, both color and texture feature vectors of query image is combined, weighted and compared with the color and texture feature vectors of each of the database images using Manhattan distance metric. The retrieved result is dependent on the weight given to each of the feature vector. We have done a detailed study of the performance of different combination of weights to color (w/sub c/) and texture (w/sub t/) features on a large database of images. Different combination weights are used in for evaluation and the results shows that texture feature vector weight (W/sub t/) in the range of W/sub c/ /spl plusmn/0.1 to w/sub c/ /spl plusmn/0.2 perform better than the other combinations.

A Survey on Content Based Image Retrieval

Abstract: Content Based Image Retrieval (CBIR) is a very important research area in the field of image processing, and comprises of low level feature extraction such as color, texture and shape and similarity measures for the comparison of images. Recently, the research focus in CBIR has been in reducing the semantic gap, between the low level visual features and the high level image semantics. This paper provides a comprehensive survey of all these aspects. This survey covers approaches used for extracting low level features; various distance measures for measuring the similarity of images, the mechanisms for reducing the semantic gap and about invariant image retrieval. In addition to these, various data sets used in CBIR and the performance measures, are also addressed. Finally, future research directions are also suggested. (I. Felci Rajam, S. Valli. A Survey on Content Based Image Retrieval. Life Sci J 2013; 10(2): 2475-2487). (ISSN: 1097-8135). http://www.lifesciencesite.com 343

Re-ranking algorithm using post-retrieval clustering for content-based image retrieval

Abstract: In this paper, we propose a re-ranking algorithm using post-retrieval clustering for content-based image retrieval (CBIR). In conventional CBIR systems, it is often observed that images visually dissimilar to a query image are ranked high in retrieval results. To remedy this problem, we utilize the similarity relationship of the retrieved results via post-retrieval clustering. In the first step of our method, images are retrieved using visual features such as color histogram. Next, the retrieved images are analyzed using hierarchical agglomerative clustering methods (HACM) and the rank of the results is adjusted according to the distance of a cluster from a query. In addition, we analyze the effects of clustering methods, querycluster similarity functions, and weighting factors in the proposed method. We conducted a number of experiments using several clustering methods and cluster parameters. Experimental results show that the proposed method achieves an improvement of retrieval effectiveness of over 10% on average in the average normalized modified retrieval rank (ANMRR) measure.

Sketch-based image retrieval using keyshapes

Abstract: Although sketch based image retrieval (SBIR) is still a young research area, there are many applications capable of exploiting this retrieval paradigm, such as web searching and pattern detection. Moreover, nowadays drawing a simple sketch query turns very simple since touch screen based technology is being expanded. In this work, we propose a novel local approach for SBIR based on detecting simple shapes which are named keyshapes. Our method works as a local strategy, but instead of detecting keypoints, it detects keyshapes over which local descriptors are computed. Our proposal based on keyshapes allow us to represent the structure of the objects in an image which could be used to increase the effectiveness in the retrieval task. Indeed, our results show an improvement in the retrieval effectiveness with respect to the state of the art. Furthermore, we demonstrate that combining our keyshape approach with a Bag of Feature approach allows us to achieve significant improvement with respect to the effectiveness of the retrieval task.