Analisis Ekstraksi Fitur Menggunakan Color Histogram, Moment, Gray Level Difference Vector<br><br>Analysis Feature Extraction Using Color Histogram, Moment, Gray Level Difference Vector

ABSTRAKSI: Image Retrieval adalah salah satu bidang dimana suatu informasi dari suatu image diambil berdasarkan fitur-fitur yang terdapat pada suatu image. Setiap image mengandung suatu informasi, seperti halnya dokumen teks. Teknik yang dipakai untuk menggali informasi bisa menggunakan teks, content, ataupun gabungan dari keduanya. Image retrieval dimana teknik pencarian citra yang didasarkan atas informasi dari isi citra tersebut disebut Content Based Image Retrieval.Pada Tugas Akhir ini, CBIR yang dikembangkan berdasarkan fitur warna, bentuk, dan tekstur. Fitur dari warna diekstraksi dengan menggunakan Color Histogram, dimana metode ini memanfaatkan nilai kemunculan dari setiap warna pada citra. Untuk ekstraksi bentuk, digunakan metode Moment Invariant. Moment invariant ini menggunakan tujuh nilai vektor yang digunakan sebagai vektor ciri yang konstan terhadap perubahan geometri. Dan untuk fitur tekstur diekstraksi dengan menggunakan metode Gray Level Difference Vector, dimana metode ini merupakan lanjutan dari metode Gray Level Co-occurrence Matrix.Hasil penelitian menunjukkan bahwa setiap metode ekstraksi fitur ini mempunyai performansi yang berbeda untuk setiap jenis citra. Pada hasil penelitian didapatkan hasil bahwa ekstraksi menggunakan fitur tekstur, mempunyai performansi yang merata terhadap semua jenis citra.Dan penggabungan esktraktor ciri yang dilakukan juga belum bisa memberikan nilai performansi yang lebih bila digunakan ciri fitur secara individu.Kata Kunci : content based image retrieval, color histogram, moment invariant, gray level difference vectorABSTRACT: Image Retrieval is one area where some information from an image taken based on the features contained in an image. Each image contains some information, such as text documents. The technique can be used to dig information using text, content, or combination of both. Image retrieval based on information from the contents of that image is called Content-Based Image Retrieval.In this final project, which was developed CBIR based on color features, shape, and texture. Color feature is extracted using Color Histogram method which takes advantage of appearance of each color in the images. For the shape feature, is extracted using Moment Invariant. It uses seven invariant moment vector value that is used as a feature vector which is constant with changes in geometry. And for the extracted texture features using Gray Level Difference Vector method, which is a continuation of the method of Gray Level Co-occurrence Matrix.The results showed that each of these feature extraction methods have different performance for each type of image. In research results showed that extraction using texture features, has a uniform performance for all types image. And the merger of these feature do not give better performance value than use individual feature.Keyword: content based image retrieval, color histogram, moment invariant, gray level difference vecto

Region Based Image Retrieval Using Ratio of Proportional Overlapping Object

In Region Based Image Retrieval (RBIR), determination of the relevant block in query region is based on the percentage of image objects that overlap with each sub-blocks. But in some images, the size of relevant objects are small. It may cause the object to be ignored in determining the relevant sub-blocks. Therefore, in this study we proposed a system of RBIR based on the percentage of proportional objects that overlap with sub-blocks. Each sub-blocks is selected as a query region. The color and texture features of the query region will be extracted by using HSV histogram and Local Binary Pattern (LBP), respectively. We also used shape as global feature by applying invariant moment as descriptor. Experimental results show that the proposed method has average precision with 74%

Sabanci-Okan system at ImageClef 2011: plant identication task

We describe our participation in the plant identication task of ImageClef 2011. Our approach employs a variety of texture, shape as well as color descriptors. Due to the morphometric properties of plants, mathematical morphology has been advocated as the main methodology for texture characterization, supported by a multitude of contour-based shape and color features. We submitted a single run, where the focus has been almost exclusively on scan and scan-like images, due primarily to lack of time. Moreover, special care has been taken to obtain a fully automatic system, operating only on image data. While our photo results are low, we consider our submission successful, since besides being our rst attempt, our accuracy is the highest when considering the average of the scan and scan-like results, upon which we had concentrated our eorts