An image mosaic is an image made up of many other images. In this paper we investigate the automatic generation of such image mosaics, using content-based image retrieval as the underlying framework. Our first contribution is to describe and evaluate a few parameters that control the quality of the mosaic image. Our second contribution is the proposal of an (automatic) measure to assess the qua...

Content Based image Retrieval (CBIR) system is one of the prominent area of study identified with wide area of applications in recognition system. The main purpose of the study is to review and analyze all the prime works in CBIR system, its efficiency in recognition rate and understand the types of challenges focused by prior research work in this area. The prime outcome of the result in this ...

Content-based image retrieval (CBIR) uses features that can be extracted from the images themselves. In previous work we have shown that using more than one representation of the images in a collection can improve the results presented to a user without changing the underlying feature extraction or search technologies[4]. In this paper we show that we can also merge the results of multiple CBIR...

Effective Graph-based Content-Based Image Retrieval Systems for Large-Scale And Small-Scale Image Databases

Distance measures like the Euclidean distance are used to measure similarity between images in content-based image retrieval. Such geometric measures implicitly assign more weighting to features with large ranges than those with small ranges. This paper discusses the effects of five feature normalization methods on retrieval performance. We also describe two likelihood ratio-based similarity me...

Content Based Image Retrival needs relevance feedback to obtain more accurate results. By building a repository with KBDA, very good results may be obtained with the use of a Query Semantic Feature Vector.

Over the last few years the interest in the research problem of indoor vs. outdoor scene classification[2] has grown significantly, due to its importance in many applications such as Content Based Image Retrieval (CBIR) or Query by Image Content (QBIC), image data organization, robotics, and photography, thus providing a strong motivation for this project. For an example, knowledge of a scene t...

Visual relationships capture a wide variety of interactions between pairs of objects in images (e.g. “man riding bicycle” and “man pushing bicycle”). Consequently, the set of possible relationships is extremely large and it is difficult to obtain sufficient training examples for all possible relationships. Because of this limitation, previous work on visual relationship detection has concentrat...

In content-based image retrieval (CBIR), the effectiveness of the low-level features depends on their capabilities in describing the high-level semantic concepts. How to properly evaluate such an effectiveness remains a challenge. In this paper, we address the evaluation problem by using the decisive feature patterns of the low-level features. Intuitively, a decisive feature pattern is a combin...

Texture is commonly used feature in most of the content-based image retrieval systems. This texture retrieval ability can be also applied to rock texture. The retrieval of the rock texture is a demanding task because of special character of rock. In this paper some existing contentbased image retrieval systems are tested with a sample set representing clearly different rock images. The recall a...