A Robust Hue Descriptor

A hue descriptor based on Logvinenko’s illuminantinvariant object colour atlas [1] is tested in terms of how well it maps hues to the hue names found in Moroney’s Colour Thesaurus [2] [3] and how well it maps hues of Munsell papers to their corresponding Munsell hue designator. Called the KSM hue descriptor, it correlates hue with the central wavelength of a Gaussian-shaped reflectance function. An important feature of this representation is that the set of hue descriptors inherits the illuminate invariant property of Logvinenko’s object colour atlas. Despite the illuminant invariance of the atlas and the hue descriptors, metamer mismatching means that colour stimulus shift [4] can occur, which will inevitably lead to some hue shifts. However, tests show that KSM hue is robust in the sense that it is much more stable under a change of illuminant than CIELAB hue

Gaussian-Based Hue Descriptors

A robust and accurate hue descriptor that is useful in modeling human color perception and for computer vision applications is explored. The hue descriptor is based on the peak wavelength of a Gaussian-like function (called a wraparound Gaussian) and is shown to correlate as well as CIECAM02 hue to the hue designators of papers from the Munsell and Natural Color System color atlases and to the hue names found in Moroney’s Color Thesaurus. The new hue descriptor is also shown to be significantly more stable under a variety of illuminants than CIECAM02. The use of wraparound Gaussians as a hue model is similar in spirit to the use of subtractive Gaussians proposed by Mizokami et al., but overcomes many of their limitations

Ekstraksi Fitur Berdasarkan Deskriptor Bentuk dan Titik Salien Untuk Klasifikasi Citra Ikan Tuna

Abstract. The manual classification of fish causes problems on accuracy and execution time. In the image of tuna, beside the shape feature, local features is also necessary to differentiate the types of fish especially which have a similar shape. The purpose of this study is to develop a new feature extraction system which integrates point of saline and the shape of descriptor to classify the image of tuna. The input image is then transformed into HSV format. Hue channel is selected for the segmentation process. Shape descriptors are extracted by using Fourier Descriptor (FD) and the saline points are extracted using Speeded Up Robust Features (SURF). The results of local features are performed by Bag of Feature (BOF). Feature integration combines shape descriptor and saline features with appropriate weight. Experimental results show that by integrating features, the classification problems of fish with similar shape can be resolved with an accuracy of classification acquired by 83.33%.Keywords: feature extraction, fourier descriptor, surf, classification, tuna fish imageAbstrak. Klasifikasi secara manual yang dilakukan berdasarkan bentuk, tekstur, dan bagian tubuh ikan dapat menimbulkan permasalahan pada akurasi dan waktu klasifikasi. Pada citra ikan tuna, selain diperlukan fitur bentuk juga diperlukan fitur lokal untuk membedakan jenis ikan terutama yang memiliki bentuk secara visual mirip. Tujuan penelitian ini adalah mengembangkan sistem ekstraksi fitur baru yang mengintegrasikan deskriptor bentuk dan titik salien untuk klasifikasi citra ikan tuna. Segmentasi diawali dengan mengambil kanal Hue pada citra HSV. Deskriptor bentuk diekstrak menggunakan Fourier Descriptor dan titik salien diekstrak menggunakan Speeded Up Robust Features. Untuk menyamakan dimensi dilakukan pemrosesan menggunakan Bag of Feature. Kedua jenis fitur yang sudah diperoleh dilakukan integrasi dengan mempertimbangkan bobot masing-masing fitur. Uji coba dilakukan pada dataset tiga jenis ikan tuna dengan 10-fold cross validation. Hasil uji coba menunjukkan dengan mengintegrasikan deskriptor bentuk dan titik salien permasalahan klasifikasi ikan tuna dengan bentuk yang mirip dapat diselesaikan dengan akurasi klasifikasi sebesar 83,33%.Kata Kunci: ekstraksi fitur, deskriptor fourier, surf, klasifikasi, citra ikan tun

Ekstraksi Fitur Berdasarkan Deskriptor Bentuk Dan Titik Salien Untuk Klasifikasi Citra Ikan Tuna

. The manual classification of fish causes problems on accuracy and execution time. In the image of tuna, beside the shape feature, local features is also necessary to differentiate the types of fish especially which have a similar shape. The purpose of this study is to develop a new feature extraction system which integrates point of saline and the shape of descriptor to classify the image of tuna. The input image is then transformed into HSV format. Hue channel is selected for the segmentation process. Shape descriptors are extracted by using Fourier Descriptor (FD) and the saline points are extracted using Speeded Up Robust Features (SURF). The results of local features are performed by Bag of Feature (BOF). Feature integration combines shape descriptor and saline features with appropriate weight. Experimental results show that by integrating features, the classification problems of fish with similar shape can be resolved with an accuracy of classification acquired by 83.33%