Comparisons of seam carving detection techniques for digital images

Nowadays, there are many mobile devices that come with a lot of sizes. The fast spread of technology allows users to experience using smartphones with different size of visual display screen. The different size of display screen of smartphones also gives a great challenge in resizing the image size to fit according to the display screen. As for that, there is a great technique called seam carving to modify an image’s proportions or sections in a way that respected to its content. Seam carving is an algorithm that is used for content aware image resizing

Optimized Image Resizing Using Seam Carving and Scaling

International audienceWe present a novel method for content-aware image resizing based on optimization of a well-defined image distance function, which preserves both the important regions and the global visual effect (the background or other decorative objects) of an image. The method operates by joint use of seam carving and image scaling. The principle behind our method is the use of a bidirectional similarity function of image Euclidean distance (IMED), while cooperating with a dominant color descriptor (DCD) similarity and seam energy variation. The function is suitable for the quantitative evaluation of the resizing result and the determination of the best seam carving number. ifferent from the previous simplex-modeapproaches, our method takes the advantages of both discrete and continuous methods. The technique is useful in image resizing for both reduction/retargeting and enlarging. We also show that this approach can be extended to indirect image resizing

Implementasi Deteksi Seam Carving Berdasarkan Perubahan Ukuran Citra Menggunakan Local Binary Patterns dan Support Vector Machine

Seam carving adalah metode yang digunakan untuk content-aware image resizing. Seam carving bertujuan untuk mengubah ukuran citra atau image resizing dengan tidak menghilangkan konten penting yang ada pada citra. Dalam bidang forensik digital, seam carving banyak dibahas khususnya tentang deteksi seam carving pada citra. Hal tersebut bertujuan untuk mengetahui apakah suatu citra sudah pernah melalui proses pengubahan ukuran menggunakan seam carving atau belum. Tugas akhir ini mengusulkan sebuah metode deteksi seam carving berdasarkan perubahan ukuran citra menggunakan Local Binary Patterns dan Support Vector Machine. Citra yang akan dideteksi dihitung variasi teksturnya menggunakan Local Binary Patterns. Proses selanjutnya adalah ekstraksi fitur dari distribusi energy yang menghasilkan 24 fitur. Data fitur citra selanjutnya dilakukan proses normalisasi. Uji coba fitur menggunakan k-fold cross validation dengan membagi data menjadi training dan testing. Selanjutnya data tersebut akan memasuki proses klasifikasi menggunakan Support Vector Machine dengan kernel Radial Basis Function. Uji coba dilakukan terhadap citra asli dan citra seam carving. Citra seam carving yang digunakan dibedakan viii berdasarkan skala rasionya yaitu 10%, 20%, 30%, 40%, dan 50%. Jumlah data yang digunakan adalah sebanyak 400 citra untuk setiap uji coba pada tiap skala rasio dengan menggunakan 10-fold cross validation. Rata-rata akurasi terbaik yang dihasilkan sebesar 73,95%. ======================================================================================================================== Seam carving is method used for content-aware image resizing. Seam carving is designed to resize the image by not eliminating the important content that is in the image. In digital forensic area, seam carving is much discussed, especially about seam carving detection in image. It aims to determine whether an image has been through the process of resizing using seam carving or not. This final project propose a method of seam carving detection based on image resizing using Local Binary Patterns and Support Vector Machine. The texture variation of image will be calculated using Local Binary Patterns. The next process is extraction process from energy distribution and produces 24 features. The feature data is then performed normalization process. The normalized features will be tested using k-fold cross validation by dividing the data into training and testing. Finally, the data will be classified using Support Vector Machine with Radial Basis Function kernel. The trial test use original image and seam carved image. Seam carved image used is differentiated by its scaling ratios of 10%, 20%, 30%, 40%, and 50%. There are 400 image used for each trial test on each scaling ratios by using 10-fold cross validation. The best average accuracy is 73,95%

Impementasi dan Analisis Seam Carving pada Content Aware Image Resizing

ABSTRAKSI: Dewasa ini pertumbuhan teknologi perangkat elektronik berkembang dengan pesat. Perangkat-perangkat elektronik tersebut memiliki ukuran tampilan yang bebeda-beda dengan sapek rasio yang berbeda-beda pula. Dengan adanya keberanekaragaman tersebut, desainer/pembuat konten harus membuat berbagai alternatif konten dengan ukuran yang berbeda untuk masing-masing perangkat. Kini halaman sebuah website telah dapat ditampilkan secara dinamis mengikuti lebar jendela perambannya. Akan tetapi, kedinamisan tersebut hanya berlaku pada konten teks saja, sementara konten-konten lain semisal gambar bersifat statis dalam hal ukuran. Di sisi lain, metode yang ada saat ini untuk melakukan pengubahan ukuran gambar (resize) hanya terpaku pada penskalaan (scaling) dan pemotongan (croping). Kedua metode ini memiliki beberapa kelemahan terhadap konten dari gambar baru yang dihasilkan. Dengan menggunakan seam carving dapat dihasilkan gambar baru yang mengubah ukuran dengan cara menghapus/menyisipkan jalur energi minimum (seam). Sebagai pembuktian, dilakukan implementasi seam carving dengan memanfaatkan operator sobel, gradient magnitude, dan pemrograman dinamis. Dari hasil implementasi diketahui bahwa metode ini telah mampu melakukan perubahan ukuran gambar berbasis konten (content aware image resizing) dengan cukup baik, meski terdapat kondisi konten gambar yang dihasilkan rusak akibat perubahan ukuran yang terlalu besar atau gambar memiliki pola tegas dan rapat. Sedangkan dari pengujian melalui kuesioner didapat bahwa mayoritas gambar yang dihasilkan telah memenuhi visual acceptability dan akurasi kemiripan gambar mampu mencapai 99% dangan Image Comparer.Kata Kunci : content aware image resizing, seam carvingABSTRACT: Nowadays, the growth of electronic device technology grows rapidly. The electronic devices have different display sizes and different aspec ratios. With such differences, the designer/content maker must make alternative contents with different sizes for each devices. Nowadays, website pages can be displayed dynamically following the browser window\u27s width. However, the dynamic is only on the text content, while the other content such as images are rigid in size. On the other hand, the methods currently available to resize the image just focus on scaling and croping. Both methods have some weaknesses against the content of the new image generated. By using Seam carving new images can be resized by deleting/inserting the minimum energy path (Seam). As proof, the implementation of Seam carving done by using Sobel operator, gradient magnitude, and dynamic programming. From the results of implementation is known that this method has capability to do content aware image Resizing pretty well, although there are conditions resulting picture content damaged by resizing image with big size changes and by image that has a sharp and tight patterns. By obtained test through questionnaire, the majority of the picture results has met visual acceptability and accuracy of similarity of the images capable to achieve up to 99% by Image Comparer.Keyword: content aware image resizing, seam carvin

Weakly- and Self-Supervised Learning for Content-Aware Deep Image Retargeting

This paper proposes a weakly- and self-supervised deep convolutional neural network (WSSDCNN) for content-aware image retargeting. Our network takes a source image and a target aspect ratio, and then directly outputs a retargeted image. Retargeting is performed through a shift map, which is a pixel-wise mapping from the source to the target grid. Our method implicitly learns an attention map, which leads to a content-aware shift map for image retargeting. As a result, discriminative parts in an image are preserved, while background regions are adjusted seamlessly. In the training phase, pairs of an image and its image-level annotation are used to compute content and structure losses. We demonstrate the effectiveness of our proposed method for a retargeting application with insightful analyses.Comment: 10 pages, 11 figures. To appear in ICCV 2017, Spotlight Presentatio

Content-aware image resizing in OpenCL

The purpose of this thesis was to test if the algorithm for content-aware image resizing runs faster on graphics processing unit in comparison to central processing unit. For that we chose content-aware image resizing algorithm called seam carving. With seam carving we can change image dimensions by finding the optimal seam which we can carve out or put in, depending on weather we want to shrink or enlarge the image. Seam is connected path from one side of the image to another and holds least important information of the image. With our testing we realized that this algorithm works best in images with monotone background. Because algorithm itself was not the purpose of this thesis we did not try to improve it. For implementation of this algorithm on graphics processing unit we used heterogeneous programming framework called OpenCL. OpenCL is a standard for heterogeneous parallel computing on cross-vendor and cross-platform hardware. We can describe OpenCL architecture with platform model, execution model, memory model and programming model. Each of them is described in details in chapter three. In chapter four we look at our implementation of seam carving algorithm. We had two approaches. One is carving one seam at the time, which means recalculating energy and its cumulative every time we carve out a seam. Second approach is carving multiple seams at a time. In this case we try to find more seams that we can carve out based on calculated energy and cumulative. We repeat the process until we get the desired image dimensions. Based on testing we realised that choosing the right work group size is really important, as well as implementation of kernels. If we choose wrong approach we can slow down its execution considerably, which is evident from the results of second approach. In this case the execution of the algorithm on central processing unit was faster then execution of it on graphics processing unit. We were more successful with implementation of first approach which runs faster on graphics processing unit then on central processing unit

Perbandingan Algoritma Shortest Path Dalam Pemrosesan Citra Digital Seam Carving

Seam carving is a method of content aware image resizing. As solutions shortest path algorithms are used to find images seams. Seam is a horizontal or vertical path of an image that has minimum energy. There are two (2) shortest path algorithms that will be discussed in this paper. This paper contains the results of shortest path algorithms comparison between Dijkstra and Directed Acyclic Graph to see which one is better than another in case of efficiency. The precomputed and recomputed methods will be compared to find the more efficient method for executing the seam carving transformation. A web application has been built for this purpose. This web app is capable of transforming image size with seam carving method. The complexity of Dijkstra and Acyclic will be compared to find which one is better. The result is Dijkstra has been won, with the O(4V) with Acyclicis O(5V). The use of precomputed and recomputed is evaluated by the conditions. If the preparation is evaluated then recomputed is more efficient, but if the preparation is not evaluated then the precomputed method is the better one and has faster performance