J.K. Aggarwal

Bio: J.K. Aggarwal is an academic researcher. The author has contributed to research in topics: Digital image processing & Color image. The author has an hindex of 1, co-authored 1 publications receiving 110 citations.

CIRES: a system for content-based retrieval in digital image libraries

Abstract: This paper presents CIRES, a new online system for a content-based retrieval in digital image libraries. Content-based image retrieval systems have traditionally used color and texture analyses. These analyses have not always achieved adequate level of performance and user satisfaction. The growing need for robust image retrieval systems has led to a need for additional retrieval methodologies. CIRES addresses this issue by using image structure in addition to color and texture. The efficacy of using structure in combination with color and texture is demonstrated.

Features for image retrieval: an experimental comparison

Abstract: An experimental comparison of a large number of different image descriptors for content-based image retrieval is presented. Many of the papers describing new techniques and descriptors for content-based image retrieval describe their newly proposed methods as most appropriate without giving an in-depth comparison with all methods that were proposed earlier. In this paper, we first give an overview of a large variety of features for content-based image retrieval and compare them quantitatively on four different tasks: stock photo retrieval, personal photo collection retrieval, building retrieval, and medical image retrieval. For the experiments, five different, publicly available image databases are used and the retrieval performance of the features is analyzed in detail. This allows for a direct comparison of all features considered in this work and furthermore will allow a comparison of newly proposed features to these in the future. Additionally, the correlation of the features is analyzed, which opens the way for a simple and intuitive method to find an initial set of suitable features for a new task. The article concludes with recommendations which features perform well for what type of data. Interestingly, the often used, but very simple, color histogram performs well in the comparison and thus can be recommended as a simple baseline for many applications.

Color image retrieval technique based on color features and image bitmap

Abstract: The field of color image retrieval has been an important research area for several decades. For the purpose of effectively retrieving more similar images from the digital image databases, this paper uses the color distributions, the mean value and the standard deviation, to represent the global characteristics of the image. Moreover, the image bitmap is used to represent the local characteristics of the image for increasing the accuracy of the retrieval system. As the experimental results indicated, the proposed technique indeed outperforms other schemes in terms of retrieval accuracy and category retrieval ability. Furthermore, the total memory space for saving the image features of the proposed method is less than Chan and Liu's method.

Learning Binary Hash Codes for Large-Scale Image Search

Abstract: Algorithms to rapidly search massive image or video collections are critical for many vision applications, including visual search, content-based retrieval, and non-parametric models for object recognition. Recent work shows that learned binary projections are a powerful way to index large collections according to their content. The basic idea is to formulate the projections so as to approximately preserve a given similarity function of interest. Having done so, one can then search the data efficiently using hash tables, or by exploring the Hamming ball volume around a novel query. Both enable sub-linear time retrieval with respect to the database size. Further, depending on the design of the projections, in some cases it is possible to bound the number of database examples that must be searched in order to achieve a given level of accuracy.