Content Based Image Recognition by Information Fusion with Multiview Features

Abstract

Abstract-Substantial research interest has been observed in the field of object recognition as a vital component for modern intelligent systems. Content based image classification and retrieval have been considered as two popular techniques for identifying the object of interest. Feature extraction has played the pivotal role towards successful implementation of the aforesaid techniques. The paper has presented two novel techniques of feature extraction from diverse image categories both in spatial domain and in frequency domain. The multi view features from the image categories were evaluated for classification and retrieval performances by means of a fusion based recognition architecture. The experimentation was carried out with four different popular public datasets. The proposed fusion framework has exhibited an average increase of 24.71% and 20.78% in precision rates for classification and retrieval respectively, when compared to state-of-the art techniques. The experimental findings were validated with a paired t test for statistical significance.

Retrieval Architecture with Classified Query for Content Based Image Recognition

“…One of the driving factors for inappropriate output has been due to reprehensible selection of keywords as query. Recent approaches of searching have emphasized on the content of the searched object rather than its name as a keyword [9][10][11][12]. The content based searching process has been facilitated by the product image which can provide the necessary knowledge for the required product based on its image contents and has been anticipated to filter out the unwanted results with higher probability.…”