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2014 IEEE JAVA IMAGE PROCESSING PROJECT Image classification using multiscale information fusion based on saliency driven nonlinear diffusion filtering
- 1. GLOBALSOFT TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmail.com Image Classification Using Multiscale Information Fusion Based on Saliency Driven Nonlinear Diffusion Filtering Abstract In this paper, we propose saliency driven image multiscale nonlinear diffusion filtering. The resulting scale space in general preserves or even enhances semantically important structures such as edges, lines, or flow-like structures in the foreground, and inhibits and smoothes clutter in the background. The image is classified using multi scale information fusion based on the original image, the image at the final scale at which the diffusion process converges, and the image at a midscale. Our algorithm emphasizes the foreground features, which are important for image classification. The background image regions, whether considered as contexts of the foreground or noise to the foreground, can be globally handled by fusing information from different scales. Experimental tests of the effectiveness of the multi scale space for the image classification are conducted on the following publicly available datasets: 1) the PASCAL
- 2. 2005dataset; 2) the Oxford 102 flowers dataset; and 3) the Oxford 17flowers dataset, with high classification rates. Existing System: In image classification, it is an important but difficult task to deal with the background information. The background treated as noise; nevertheless, in some cases the background provides a context, which may increase the performance of image classification. Experimentally analyzed the influence of the background on image classification. They demonstrated that although the background may have correlations with the foreground objects, using both the background and foreground features for learning and recognition yields less accurate results than using the foreground features alone. Overall, the background information was not relevant to image classification. Proposed System We propose to classify images using the saliency driven multi-scale image representation. Images whose foregrounds are clearer than their backgrounds are more likely to be correctly classified at a large scale, and images whose backgrounds are clearer are more likely to be correctly classified at a small scale. So, information from different scales can be used to acquire more accurate image classification results. Advantage
- 3. No other work which applies nonlinear diffusion filtering to image classification.. First, the nonlinear diffusion-based multi scale space can preserve or enhance semantically important image structures at large scales. Second, our method can deal with the background information no matter whether it is a context or noise, and then can be adapted to backgrounds which change over time. Third, our method can partly handle cases in which the saliency map is incorrect, by including the original image at scale 0 in the set of scaled images used for classification. Modules: Original Image Scales tm Scales TM Multi scale Diffusion(Saliency) Original Image It contains original image with large background for saliency multi scale detection.
- 4. Tm and TM: Multi-scale fusion obtains more accurate results than those obtained using the individual scales Tm or TM. This indicates that the three scales include complementary information, and their fusion can improve the classification results. However, because the original image is included in the fusion, correct final classification results are obtained.
- 5. Multi Scale Diffusion (Saliency): Saliency maps, the foreground regions were correctly detected. Our saliency driven nonlinear diffusion preserved their foreground regions and largely smoothed the background regions. Therefore, at scales Tm and TM in which the backgrounds were filtered out, the images were correctly classified. This produces a correct classification by multi-scale fusion.
- 6. System Specification Hardware Requirements: • System : Pentium IV 2.4 GHz. • Hard Disk : 40 GB. • Floppy Drive: 1.44 Mb. • Monitor : 14’ Colour Monitor. • Mouse : Optical Mouse. • Ram : 512 Mb. Software Requirements: • Operating system : Windows 7. • Coding Language : ASP.Net with C# • Data Base : SQL Server 2008.
- 7. Conclusion In this paper, we have proposed saliency driven multi-scale nonlinear diffusion filtering, by modifying the mathematical equations for nonlinear diffusion filtering, and determining the diffusion parameters using the saliency detection results. We have further applied this new method to image classification. The saliency driven nonlinear multi-scale space preserves and even enhances important image local structures, such as lines and edges, at large scales. Multi-scale information has been fused using a weighted function of the distances between images at different scales. The saliency driven multi-scale representation can include information about the background in order to improve image classification. Experiments have been conducted on widely used datasets, namely the PASCAL2005 dataset, the Oxford 102 flowers dataset, and the Oxford 17 flowers dataset. The results have demonstrated that saliency driven multi-scale information fusion improves the accuracy of image classification.