Query by Image Content using Color-Texture Features Extracted from Haar Wavelet Pyramid

Abstract

The paper presents the Wavelet Pyramid based image retrieval techniques [1] using Haar transform. Here content based image retrieval (CBIR) is done using the image feature set extracted from Haar Wavelets applied on the image at various levels of decomposition. Here the database image features are extracted by applying Haar Wavelets on gray plane (average of red, green and blue) and color planes (red, green and blue components). The techniques Gray-Haar Wavelets and Color-Haar Wavelets are tested on image database having 11 categories with total 1000 images. Total 55 queries are fired on the database. The results show that precision and recall of Haar Wavelets are better than complete Haar transform based CBIR, which proves that Haar Wavelets gives better discrimination capability in image retrieval at higher query execution speed, per higher level Haar Wavelets. Color-Haar Wavelets based CBIR have greater precision and recall than Gray-Haar Wavelets based CBIR. The Haar Wavelets level-5 outperforms other Haar Wavelets, because the higher level Haar Wavelets are giving very coarse color-texture features while the lower level are representing very fine color-texture features which are less useful to differentiate the images in image retrieval.