Implementasi Metode Kombinasi Histogram of Oriented Gradients dan Hierarchical Centroid untuk Sketch Based Image Retrieval

Teknik pencarian gambar yang saat ini umum digunakan masih berbasis teks atau text based search seperti pada mesin pencarian Google Image, Yahoo, dan lain sebagainnya. Namun metode ini masih kurang efektif karena nama dari sebuah file tidak dapat merepresentasikan isinya, oleh karena itu diperlukan pemilihan kata kunci yang benar-benar tepat agar hasil yang diinginkan dapat ditampilkan dengan baik. Salah satu teknik pencarian gambar yang saat ini sedang diteliti adalah Sketch-Based Image Retrieval (SBIR). Dengan teknik ini user dapat menginputkan sketsa gambar atau user dapat menggambarkan obyek pada area yang disediakan lalu sistem akan melakukan pencocokkan sketsa dengan database gambar. Untuk mengimplementasikan teknik ini digunakan metode kombinasi Histogram of Oriented Gradient dan Hierarchical Centroid. Tahapan implementasi teknik tersebut yaitu, yang pertama melakukan preprocessing pada gambar dengan cara mendeteksi tepi obyek lalu membuat citra menjadi hitam putih. Yang kedua melakukan ektraksi fitur menggunakan Histogram of Oriented Gradients dan Hierarchical Centroid dan menghasilkan fitur vektor. Yang terakhir menghitung jarak kedekatan antara gambar yang diuji dengan gambar yang terdapat dalam database menggunakan Euclidean Distance. Hasil Euclidean Distance kemudian diurutkan secara ascending dan dikembalikan sejumlah gambar yang jaraknya terdekat. Hasil temu kembali menghasilkan nilai Average Normalized Modified Retrieval Rank sebesar 0,35 dan nilai presisi dan recall sebesar 78 % dan akurasi sebesar 96%

Deep Sketch Hashing: Fast Free-hand Sketch-Based Image Retrieval

Free-hand sketch-based image retrieval (SBIR) is a specific cross-view retrieval task, in which queries are abstract and ambiguous sketches while the retrieval database is formed with natural images. Work in this area mainly focuses on extracting representative and shared features for sketches and natural images. However, these can neither cope well with the geometric distortion between sketches and images nor be feasible for large-scale SBIR due to the heavy continuous-valued distance computation. In this paper, we speed up SBIR by introducing a novel binary coding method, named \textbf{Deep Sketch Hashing} (DSH), where a semi-heterogeneous deep architecture is proposed and incorporated into an end-to-end binary coding framework. Specifically, three convolutional neural networks are utilized to encode free-hand sketches, natural images and, especially, the auxiliary sketch-tokens which are adopted as bridges to mitigate the sketch-image geometric distortion. The learned DSH codes can effectively capture the cross-view similarities as well as the intrinsic semantic correlations between different categories. To the best of our knowledge, DSH is the first hashing work specifically designed for category-level SBIR with an end-to-end deep architecture. The proposed DSH is comprehensively evaluated on two large-scale datasets of TU-Berlin Extension and Sketchy, and the experiments consistently show DSH's superior SBIR accuracies over several state-of-the-art methods, while achieving significantly reduced retrieval time and memory footprint.Comment: This paper will appear as a spotlight paper in CVPR201

Online sketch-based image retrieval using keyshape mining of geometrical objects

Online image retrieval has become an active information-sharing due to the massive use of the Internet. The key challenging problems are the semantic gap between the low-level visual features and high-semantic perception and interpretation, due to understating complexity of images and the hand-drawn query input representation which is not a regular input in addition to the huge amount of web images. Besides, the state-of-art research is highly desired to combine multiple types of different feature representations to close the semantic gap. This study developed a new schema to retrieve images directly from the web repository. It comprises three major phases. Firstly a new online input representation based on pixel mining to detect sketch shape features and correlate them with the semantic sketch objects meaning was designed. Secondly, training process was developed to obtain common templates using Singular Value Decomposition (SVD) technique to detect common sketch template. The outcome of this step is a sketch of variety templates dictionary. Lastly, the retrieval phase matched and compared the sketch with image repository using metadata annotation to retrieve the most relevant images. The sequence of processes in this schema converts the drawn input sketch to a string form which contains the sketch object elements. Then, the string is matched with the templates dictionary to specify the sketch metadata name. This selected name will be sent to a web repository to match and retrieve the relevant images. A series of experiments was conducted to evaluate the performance of the schema against the state of the art found in literature using the same datasets comprising one million images from FlickerIm and 0.2 million images from ImageNet. There was a significant retrieval in all cases of 100% precision for the first five retrieved images whereas the state of the art only achieved 88.8%. The schema has addressed many low features obstacles to retrieve more accurate images such as imperfect sketches, rotation, transpose and scaling. The schema has solved all these problems by using a high level semantic to retrieve accurate images from large databases and the web

Identificación de personas mediante cámaras cenitales: generación de algoritmos de reconocimiento de personas captadas cenitalmente

En este proyecto se lleva acabo el diseño y la implementación de un sistema de detección de personas captadas mediante cámaras localizadas cenitalmente . El objet ivo es realizar una comparación entre algoritmos existentes para la detección de personas , y estimar que tipo de características y que tipo de clasificación se ajusta mejor a la detección que se quiere realizar. Se ha realizado la comparación entre dos mét odos de detección de objetos , entrenados con el fin de detectar personas captadas cenitalmente, analizando la tas a de errores que tiene cada método, la cantidad de falsos positivos y de falsos negativos detectados por cada método, tanto en un set de imágen es como en una secuencia de video, para poder conocer su precisión y sus debilidades. Todas las pruebas se realizaron con imágenes captadas en zonas de interior. La captación se ha realizado mediante una cámara “ LI - OV560 - USB - 72” y un PC. Todo el software s e ha desarrollado en lenguaje de programación C++ y el procesado de imagen se ha realizado con ayuda de la librería externa OpenCV, una librería de visión por computador de código abierto disponible para varios sistemas operativos. El software desarrollado consta de una aplicación que permite la identificación de personas en tiempo r eal, pudiendo alternar entre los dos métodos que se a nalizan a lo largo del proyecto.This Project develops the design and implementation of a people detector system , capturing people from aeri al view. The objective is perform a comparison between existent algorithms to detect people , and find out which kind of features and which kind of classification fits better to get the desired detection. A comparison between two object detection methods is made, training each one with t he purpose of detect people in aeri al view, analysing the error rate of each method, the number of false positives and false negatives detected by each method, tested with a set of images and also w ith a video sequence, so we can know the precision and the weakness of each method. All the test were made with interior images. The images were captured by one camera “ LI - OV560 - USB - 72” and a PC. All the software has been developed in C++ programming lang uage , and the image processing has be en done with the OpenCV external library, an open source library for computer vision available in different operative systems . The developed software c onsist mainly of an application that allows the people identificati on in real time, giving the choice to choose between the two methods analysed throughout this p roject