Features Conduction Neural Response and Its Application in Content-Based Image Retrieval

Content Based Image Retrieval based on Color, Texture and Shape features using Image and its complement

Color, texture and shape information have been the primitive image descriptors in content based image retrieval systems. This paper presents a novel framework for combining all the three i.e. color, texture and shape information, and achieve higher retrieval efficiency using image and its complement. The image and its complement are partitioned into non-overlapping tiles of equal size. The feat...

Prosemantic Features for Content-Based Image Retrieval

We present here, an image description approach based on prosemantic features. The images are represented by a set of low-level features related to their structure and color distribution. Those descriptions are fed to a battery of image classifiers trained to evaluate the membership of the images with respect to a set of 14 overlapping classes. Packing together the scores vectors of prosemantic ...

Content-Based Image Retrieval Using Multiple Features

Algorithms of Content-Based Image Retrieval (CBIR) have been well developed along with the explosion of information. These algorithms are mainly distinguished based on feature used to describe the image content. In this paper, the algorithms that are based on color feature and texture feature for image retrieval will be presented. Color Coherence Vector based image retrieval algorithm is also a...

Content based Image Retrieval using Combined Features

Content Based Image Retrieval is the application of computer vision techniques to the image retrieval problem of searching for digital images in large databases. The method of CBIR discussed in this paper can filter images based their content and would provide a better indexing and return more accurate results. In this paper techniques to retrieve image based on their content namely color, text...

: Fuzzy aggregation of image features in content-based image retrieval