A Hybrid Proposed Framework for Object Detection and Classification
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초록
The object classification using the images’ contents is a big challenge in computer vision. The superpixels’information can be used to detect and classify objects in an image based on locations. In this paper, weproposed a methodology to detect and classify the image's pixels' locations using enhanced bag of words(BOW). It calculates the initial positions of each segment of an image using superpixels and then ranks itaccording to the region score. Further, this information is used to extract local and global features using ahybrid approach of Scale Invariant Feature Transform (SIFT) and GIST, respectively. To enhance theclassification accuracy, the feature fusion technique is applied to combine local and global features vectorsthrough weight parameter. The support vector machine classifier is a supervised algorithm is used forclassification in order to analyze the proposed methodology. The Pascal Visual Object Classes Challenge 2007(VOC2007) dataset is used in the experiment to test the results. The proposed approach gave the results inhigh-quality class for independent objects’ locations with a mean average best overlap (MABO) of 0.833 at1,500 locations resulting in a better detection rate. The results are compared with previous approaches and itis proved that it gave the better classification results for the non-rigid classes.
Abstract
The object classification using the images’ contents is a big challenge in computer vision. The superpixels’information can be used to detect and classify objects in an image based on locations. In this paper, weproposed a methodology to detect and classify the image's pixels' locations using enhanced bag of words(BOW). It calculates the initial positions of each segment of an image using superpixels and then ranks itaccording to the region score. Further, this information is used to extract local and global features using ahybrid approach of Scale Invariant Feature Transform (SIFT) and GIST, respectively. To enhance theclassification accuracy, the feature fusion technique is applied to combine local and global features vectorsthrough weight parameter. The support vector machine classifier is a supervised algorithm is used forclassification in order to analyze the proposed methodology. The Pascal Visual Object Classes Challenge 2007(VOC2007) dataset is used in the experiment to test the results. The proposed approach gave the results inhigh-quality class for independent objects’ locations with a mean average best overlap (MABO) of 0.833 at1,500 locations resulting in a better detection rate. The results are compared with previous approaches and itis proved that it gave the better classification results for the non-rigid classes.
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