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.

Rate-efficient, real-time cd cover recognition on a camera-phone

Abstract: Automatic CD cover recognition has interesting applications for comparison shopping and music sampling. We demonstrate a real-time CD cover recognition using a cameraphone. By snapping a picture of a CD cover with her cameraphone, a user can conveniently retrieve information related to the CD. Robust image feature extraction is applied to overcome the image distortions in the query photo. To limit the amount of data transmitted over a wireless network, we compress the query image or features extracted from the query image. On the database side, fast and reliable image matching against a database of 10,000 CD covers is accomplished using a scalable vocabulary tree.

Content-based image retrieval using hierarchical temporal memory

Abstract: Several querying interfaces for content-based image retrieval (CBIR) are reviewed and a new CBIR system is introduced that uses Hierarchical Temporal Memory for the automatic indexing of architectural images and provides a sketch-based and iconic index querying interface. Experimentation shows the system is robust for recognizing query images under varying amounts of noise, distortion, occlusion, blurring, and affine transformation.

Frequency layered color indexing for content-based image retrieval

Abstract: Image patches of different spatial frequencies are likely to have different perceptual significance as well as reflect different physical properties. Incorporating such concept is helpful to the development of more effective image retrieval techniques. We introduce a method which separates an image into layers, each of which retains only pixels in areas with similar spatial frequency characteristics and uses simple low-level features to index the layers individually. The scheme associates indexing features with perceptual and physical significance thus implicitly incorporating high level knowledge into low level features. We present a computationally efficient implementation of the method, which enhances the power and at the same time retains the simplicity and elegance of basic color indexing. Experimental results are presented to demonstrate the effectiveness of the method.

Combining visual features of an image at different precision value of unsupervised content based image retrieval

Abstract: Image Retrieval is a technique of searching, browsing, and retrieving the images from an image database. There are two types of different image retrieval techniques namely text based image retrieval and content based image retrieval techniques. Text-Based image retrieval uses traditional database techniques to manage images. Content-based image retrieval (CBIR) uses the visual features of an image such as color, shape, texture, and spatial layout to represent and index the image. CLUE (CLUster based image rEtrieval) is a well known CBIR technique retrieves the images by clustering approach [4]. In this paper, we propose a CBIR system that also retrieves images by clustering just like CLUE. But, the proposed system combines all the features (shape, color, and texture) with some percentage of all features value for the purpose. In this paper we proposed two methods of CBIR by combining some percentage value of two features namely color-texture features and color-shape features and we also take the union of these two features. This combination of features provides a robust feature set for image retrieval. We evaluated the performance of proposed methods at different precision value of the image retrieval on each category of image database. We compared the performance of the proposed system with the two other existing CBIR systems namely UFM and CLUE at precision 100. We experimented with COREL standard database of images and experimentally, we find that the proposed system is better than the other two existing systems, and at smaller precision value result outperforms in almost each category of images.

Automatic blur detection for meta-data extraction in content-based retrieval context

Abstract: During the last few years, image by content retrieval is the aim of many studies. A lot of systems were introduced in order to achieve image indexation. One of the most common method is to compute a segmentation and to extract different parameters from regions. However, this segmentation step is based on low level knowledge, without taking into account simple perceptual aspects of images, like the blur. When a photographer decides to focus only on some objects in a scene, he certainly considers very differently these objects from the rest of the scene. It does not represent the same amount of information. The blurry regions may generally be considered as the context and not as the information container by image retrieval tools. Our idea is then to focus the comparison between images by restricting our study only on the non blurry regions, using then these meta data. Our aim is to introduce different features and a machine learning approach in order to reach blur identification in scene images.