A hybrid content based image retrieval system using log-gabor filter banks

In this paper, a new efficient image retrieval system using sequential process of three stages with filtering technique for the feature selection is proposed. In the first stage the color features are extracted using color histogram method and in the second stage the texture features are obtained using log-Gabor filters and in the third stage shape features are extracted using shape descriptors using polygonal fitting algorithm. The proposed log-Gabor filter in the second stage has advantages of retrieving images over regular Gabor filter for texture. It provides better representation of the images. Experimental evaluation of the proposed system shows improved performance in retrieval as compared to other existing systems in terms of average precision and average recall

Optimized Fuzzy C-means Clustering Methods for Defect Detection on Leather Surface

In this paper, captured images are segmented for the defective part, that is used for the further process of grading the quality of the products using automated inspection systems employed in industries such as leather, fabrics, textiles, tiles... etc.. These industries are the greatest conventional industries that need automatic detection systems as a basic part in diminishing investigation time and expanding production rate. Initially in this work, the input image is wet blue leather fed into a contrast enhancement process that improves the visibility of the image features. This contrast-enhanced image is employed with segmentation process that utilizes Fuzzy C-means algorithm (FCM) technique. This paper proposes two different optimization techniques, Grey Wolf Optimization (GWO) & Monarch Butterfly Optimization (MBO) for executing centroid optimization in FCM and results are compared with Modified Region Growing with GWO of leather segmentation method. The results exemplify that incorporation of optimization technique with FCM has a quite evident impact on segmentation accuracy of 96.90% over context techniques

Optimized Fuzzy C-means Clustering Methods for Defect Detection on Leather Surface

833-836In this paper, captured images are segmented for the defective part, that is used for the further process of grading the quality of the products using automated inspection systems employed in industries such as leather, fabrics, textiles, tiles... etc.. These industries are the greatest conventional industries that need automatic detection systems as a basic part in diminishing investigation time and expanding production rate. Initially in this work, the input image is wet blue leather fed into a contrast enhancement process that improves the visibility of the image features. This contrast-enhanced image is employed with segmentation process that utilizes Fuzzy C-means algorithm (FCM) technique. This paper proposes two different optimization techniques, Grey Wolf Optimization (GWO) & Monarch Butterfly Optimization (MBO) for executing centroid optimization in FCM and results are compared with Modified Region Growing with GWO of leather segmentation method. The results exemplify that incorporation of optimization technique with FCM has a quite evident impact on segmentation accuracy of 96.90% over context techniques