Abstract
In this paper, computer vision based Content-Based Image Classification systems have been described which are useful in various service and product industries. We have proposed Confidence Co-occurrence Matrix, which is a modification of Generalized Co-occurrence Matrix. The proposed framework merges properties of Confidence Co-occurrence Matrix along with other features such as RGB and HSV Histograms, Local Binary Pattern and Canny’s edge detection approach. Proposed approach creates a fixed- size descriptor of size 1632. Once a feature vector has been constructed, classification is performed using Linear Support Vector Machine. The System is tested on four different wellknown datasets namely, sport events Database, Flavia Leaf Dataset, Leeds Butterfly Dataset and Birds Dataset . The proposed system is implemented in MATLAB and achieves an average class accuracy of 96%, 99%,95% and 95% for the four datasets respectively
Highlights
In the last decade, Content-based Image classification has attracted people from both academia and industry
The remainder of this paper is organized as: Section 2 covers literature review, Section 3 gives a detailed view of the proposed system and explains Confidence Co-occurrence Matrix in detail, Section 4,5,6 and 7 discuss datasets and experimental setup, Section 8 describes results and comparisons among chosen classifiers and Section 9 concludes our work
The above mentioned combination of features is used first time for classification and measures are discussed in terms of precision, recall, sensitivity, specificity and F-Score
Summary
Content-based Image classification has attracted people from both academia and industry. The task is to automatically classify an image by feature extraction and assignment of a label. The Content-based Image Classification produces many successful applications in areas like Agriculture, Music, Bio-metric, and Medical science to name the few. Any activity done under specific environment, with the help of some objects, is widely known as ’event’. There are three types of dataset available for event classification: video, audio, and image. As many people capture events by clicking pictures, it is not always necessary to have the video information available. The event recognition from images is more challenging task as compared to background scene classification and object recognition. The remainder of this paper is organized as: Section 2 covers literature review, Section 3 gives a detailed view of the proposed system and explains Confidence Co-occurrence Matrix in detail, Section 4,5,6 and 7 discuss datasets and experimental setup, Section 8 describes results and comparisons among chosen classifiers and Section 9 concludes our work
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