Visual Word

About: Visual Word is a research topic. Over the lifetime, 12332 publications have been published within this topic receiving 308523 citations.

A new approach to large-scale image recognition for visual search engines

Abstract: Image and pattern recognition have become a significant task in recent years when most mobile communication devices have integrated cameras. The bag-of-words (BOW) approach is commonly used in information retrieval algorithms, and although originally developed for text, it has been adapted also for visual search. In spite of the similarity, there are different challenges - less effective weighting algorithms and quantization errors reduce the discrimination power of a single visual word. Moreover, the standard BOW algorithm completely ignores the geometric relationship between visual words. In this paper we introduce an efficient retrieval algorithm that accounts for geometric consistency constraints between two sets of local features that can be integrated with an inverted file. The algorithm significantly improves the initial ranking of the search results, promoting the more suitable candidates to the top of the results list. Application of the new algorithm shows that the proposed method outperforms presently available BOW retrieval algorithms even when followed by full geometric verification. Our conclusion is that the new algorithm and its associated data structure could be instrumental in improving image retrieval tasks.

Algorithm of Image Retrieval Based on Color-Space

Abstract: This paper deals with a new image retrieval method which can improve the retrieving speed by extracting representative color in HIS color pattern and enhance the retrieval precision and quality by providing different parameter of histogram for each segment. Experiments show that ideal retrieval results can be obtained with this method.

Color based Image Retrieval using Supervised Learning

Abstract: The study of Content Based Image Retrieval also has the important meaning for impelling and enriching the theory of signal and information processing.-Many existing color based image search techniques searches image based on color of entire image irrespective of foreground and background which have disadvantage of retrieving images based on dominant color in the image (mostly background) but many a time user might be interested in foreground information. We are focusing on image search based on foreground color. Obviously since locating object accurately is one of the most challenging and open problem in computer vision, in this work we limit our self to human dress as foreground. We are able to extract images with excellent precision and recall on our own dataset collected from web in this paper content based image retrieval is a promising approach to search image database by means of image features such as color, texture, shape, pattern or any combinations of them.

Recognition of Enhanced Images

Abstract: Image enhancement such as adjusting brightness and contrast is central to improving human visualization of images’ content. Images in desired enhanced quality facilitate analysis, interpretation, classification, information exchange, indexing and retrieval. The adjustment process, guided by diverse enhancement objectives and subjective human judgment, often produces various versions of the same image. Despite the preservation of content under these operations, enhanced images are treated as new in most existing techniques via their widely different features. This leads to difficulties in recognition and retrieval of images across application domains and user interest. To allow unrestricted enhancement flexibility, accurate identification of images and their enhanced versions is therefore essential. In this paper, we introduce a measure that theoretically guarantees the identification of all enhanced images originated from one. In our approach, images are represented by points in multidimensional intensity-based space. We show that points representing images of the same content are confined in a well-defined area that can be identified by a so-devised formula. We evaluated our technique on large sets of images from various categories, including medical, satellite, texture, color images and scanned documents. The proposed measure yields an actual recognition rate approaching 100% in all image categories, outperforming other well-known techniques by a wide margin. Our analysis at the same time can serve as a basis for determining the minimum criterion a similarity measure should satisfy. We discuss also how to apply the formula as a similarity measure in existing systems to support general image retrieval.