Sketch-based Image Retrieval System Research Articles

Sketch-Based Image Retrieval Using Novel Edge Detector and Feature Descriptor

With the explosive increase of digital images, intelligent information retrieval systems have become an indispensable tool to facilitate users’ information seeking process. Although various kinds of techniques like keyword-/content-based methods have been extensively investigated, how to effectively retrieve relevant images from a large-scale database remains a very challenging task. Recently, with the wide availability of touch screen devices and their associated human-computer interaction technology, sketch-based image retrieval (SBIR) methods have attracted more and more attention. In contrast to keyword-based methods, SBIR allows users to flexibly manifest their information needs into sketches by drawing abstract outlines of an object/scene. Despite its ease and intuitiveness, it is still a nontrivial task to accurately extract and interpret the semantic information from sketches, largely because of the diverse drawing styles of different users. As a consequence, the performance of existing SBIR systems is still far from being satisfactory. In this paper, we introduce a novel sketch image edge feature extraction algorithm to tackle the challenges. Firstly, we propose a Gaussian blur-based multiscale edge extraction (GBME) algorithm to capture more comprehensive and detailed features by continuously superimposing the edge filtering results after Gaussian blur processing. Secondly, we devise a hybrid barycentric feature descriptor (RSB-HOG) that extracts HOG features by randomly sampling points on the edges of a sketch. In addition, we integrate the directional distribution of the barycenters of all sampling points into the feature descriptor and thus improve its representational capability in capturing the semantic information of contours. To examine the efficiency of our method, we carry out extensive experiments on the public Flickr15K dataset. The experimental results indicate that the proposed method is superior to existing peer SBIR systems in terms of retrieval accuracy.

IMPLEMENTING SKETCH-BASED IMAGE RETRIEVAL SYSTEM USING EDGE DETECTION AND SIFT ALGORITHM

Throughout the past few years, several researchers have introduced various methods and various algorithms for a precise and dependable sketch-based image retrieval system. In this paper, a proposed sketch-based image retrieval system is introduced. The framework goes over two phases: creating the sketch dataset phase and implementing SIFT (scale invariant feature transform) algorithm. The sketch dataset was created by selecting 100 colored image passed through canny edge detection operator. The system tends to enter a line-based/hand-drawing sketch and applies the SIFT algorithm to match between the input sketch and all sketches in the dataset. SIFT is one of the main efficient algorithms that are used to make description and matching, since it works on large keypoints. This system retrieves images depending on sketch image, and the result of matching will retrieve images that are approximate the entered sketch. The proposed system is assessed according to the measures that are utilized in detection, description and matching grounds, which are precision, recall and accuracy measures. The system showed (96 %) accuracy for line based sketches and (84%) for hand drawing where the detection was identical.

Feature Analysis of Sketch Based Image Retrieval System

Feature Analysis of Sketch Based Image Retrieval System

Orientation Feature Transform Model for Image Retrieval in Sketch Based Image Retrieval System

This paper focus on Image retrieval using Sketch based image retrieval system. The low complexity model for image representation has given the sketch based image retrieval (SBIR) a optimal selection for next generation application in low resource environment. The SBIR approach uses the geometrical region representation to describe the feature and utilize for recognition. In the SBIR model, the features represented define the image. Towards the improvement of SBIR recognition performance, in this paper a new invariant modeling using “orientation feature transformed modeling” is proposed. The approach gives the enhancement of invariant property and retrieval performance improvement in transformed domain. The experimental results illustrate the significance of invariant orientation feature representation in SBIR over the conventional models.

A study on the comparison of various retrieval methods based on the measured accuracy

In this day and age, innovation is improving step by step, the most upgraded research zone in computerized image preparing is image retrieval system. The procedures utilized for recovering image based on content, the substance as content, sketch, shading and shape that can be depict the image. Here we introduce different image retrieval strategies which is utilized as sketch content. So the system is alluded to as Sketch Based Image Retrieval System (SBIR). In this current paper, we actualize EHD, Integrated EHD& HOD and HOG algorithms and provide the examination of 3 algorithms based on their measured accuracy. SBIR is invaluable than absolutely message base image search. The retrieval system utilizing sketches can be basic and powerful in our every day life, for example, Medical finding, computerized library, search engines, wrongdoing aversion, geographical information, photograph sharing locales and remote sensing systems.

Data augmentation-assisted deep learning of hand-drawn partially colored sketches for visual search.

In recent years, image databases are growing at exponential rates, making their management, indexing, and retrieval, very challenging. Typical image retrieval systems rely on sample images as queries. However, in the absence of sample query images, hand-drawn sketches are also used. The recent adoption of touch screen input devices makes it very convenient to quickly draw shaded sketches of objects to be used for querying image databases. This paper presents a mechanism to provide access to visual information based on users’ hand-drawn partially colored sketches using touch screen devices. A key challenge for sketch-based image retrieval systems is to cope with the inherent ambiguity in sketches due to the lack of colors, textures, shading, and drawing imperfections. To cope with these issues, we propose to fine-tune a deep convolutional neural network (CNN) using augmented dataset to extract features from partially colored hand-drawn sketches for query specification in a sketch-based image retrieval framework. The large augmented dataset contains natural images, edge maps, hand-drawn sketches, de-colorized, and de-texturized images which allow CNN to effectively model visual contents presented to it in a variety of forms. The deep features extracted from CNN allow retrieval of images using both sketches and full color images as queries. We also evaluated the role of partial coloring or shading in sketches to improve the retrieval performance. The proposed method is tested on two large datasets for sketch recognition and sketch-based image retrieval and achieved better classification and retrieval performance than many existing methods.

DeepSketch 3

Freehand sketches are a simple and powerful tool for communication. They are easily recognized across cultures and suitable for various applications. In this paper, we use deep convolutional neural networks (ConvNets), state-of-the-art in the field of sketch recognition, to address several applications of automatic sketch processing: complete and partial sketch recognition, sketch retrieval using query-by-example (QbE), and sketch-based image retrieval (SBIR) i.e the retrieval of images using a QbE paradigm but where the query is a sketch. We first focus on improving sketch recognition. For this purpose we compare different ConvNet architectures, training paradigms and data fusion schemes. This enabled us to outperform previous state-of-the-art in two large scale benchmarks for sketch classification. We achieved a mean average accuracy of 79.18% for the TU-Berlin sketch benchmark and 93.02% for the sketchy database. For partial sketch recognition, we were able to produce a system that achieves a mean average accuracy of 52.58% with only 40% of the strokes. We then conduct a comprehensive study of ConvNets features to enhance sketch retrieval and image retrieval, using a kNN similarity search paradigm in the ConvNet feature space. For the sketch retrieval tasks, we compare the performance obtained with features extracted from various depths (ConvNet layers) using one of the best performing model from the previous work. For the sketch-based image retrieval (SBIR), a sketch query is used to retrieve images of objects that belong to the same category, or even with a shape and pose close to the sketch query. The main challenge in the field of SBIR is to obtain efficient cross-domain features for sketch-image similarity measure. For this, besides comparing features extracted from different depth, we additionally compare different training approaches (some novel) for the ConvNets applied to sketches and images. Eventually, our best SBIR system achieves state-of-the-art results on the sketchy database (close to 40% recall at k = 1).

Enhancing Sketch-Based Image Retrieval by Re-Ranking and Relevance Feedback.

A sketch-based image retrieval often needs to optimize the tradeoff between efficiency and precision. Index structures are typically applied to large-scale databases to realize efficient retrievals. However, the performance can be affected by quantization errors. Moreover, the ambiguousness of user-provided examples may also degrade the performance, when compared with traditional image retrieval methods. Sketch-based image retrieval systems that preserve the index structure are challenging. In this paper, we propose an effective sketch-based image retrieval approach with re-ranking and relevance feedback schemes. Our approach makes full use of the semantics in query sketches and the top ranked images of the initial results. We also apply relevance feedback to find more relevant images for the input query sketch. The integration of the two schemes results in mutual benefits and improves the performance of the sketch-based image retrieval.

Utilizing Effective Way of Sketches for Content-Based Image Retrieval System

paper reviews the development of content based image retrieval system (CBIR) and sketch based image retrieval system (SBIR). In this paper, we present the problems and challenges concerned with the design and the creation of CBIR systems, which is based on a free hand sketch (i.e. SBIR). The use of the existing methods, describe a possible results, how to design and implement a task specific descriptor, which can handle the informational gap between a sketch a colored image to make an opportunity for the efficient search. The CBIR system first computes the similarity between the query and the images stored in the database. This paper outlines a description of primitive features of images like textures, colors and shapes. The review of the feature extraction, feature based matching and indexing which represent the base of retrieving images and it allows the comparison of database images with queries containing various levels of detail, thanks to a hierarchical representation of the database images. In this paper, we create the mosaic of the images and compared the methods of matching sketches descriptor.

A Review on Matching For Sketch Technique

In the area of digital image processing content based image retrieval (CBIR) is one of the most popular, researches raising area. Many tools like Google, Yahoo & other used textual annotation of images. The principle work that was done by CBIR is to extract visual content of image intuitively like color, shape & texture. Additionally sketch based image retrieval system (SBIR) is also plays very imperative role within the content based image retrieval system (CBIR). The system including problem & challenges concerned with design and constitution of content based image retrieval system. The vital objective that holds the information gap between a sketch & color image we have to design & appliance task specific descriptor. The system states that a description about a possible solution for the implementation of descriptor to carry a special sequence of altering the steps of the reconstruction of full color image sketch can be compared. For the preparation of gallery can take a shop images with possible application for forensic sketch identical images. Keywords: Sketch-based image retrieval, Image databases, Image descriptors, CBIR, SBIR.

Sketch-Based Image Retrieval: Benchmark and Bag-of-Features Descriptors

We introduce a benchmark for evaluating the performance of large-scale sketch-based image retrieval systems. The necessary data are acquired in a controlled user study where subjects rate how well given sketch/image pairs match. We suggest how to use the data for evaluating the performance of sketch-based image retrieval systems. The benchmark data as well as the large image database are made publicly available for further studies of this type. Furthermore, we develop new descriptors based on the bag-of-features approach and use the benchmark to demonstrate that they significantly outperform other descriptors in the literature.

Deformation tolerant generalized Hough transform for sketch-based image retrieval in complex scenes

Sketch-based image retrieval systems need to handle two main problems. First of all, they have to recognize shapes similar but not necessarily identical to the user's query. Hence, exact object ide...

Deformation tolerant generalized Hough transform for sketch-based image retrieval in complex scenes

Sketch-based image retrieval systems need to handle two main problems. First of all, they have to recognize shapes similar but not necessarily identical to the user’s query. Hence, exact object identification techniques do not fit in this case. The second problem is the selection of the image features to compare with the user’s sketch. In domain-independent visual repositories, real-life images with non-uniform background and possible occluding objects make this second task particularly hard. We address the second problem proposing a variant of the well-known Generalized Hough Transform (GHT), which is a robust object identification technique for unsegmented images. Moreover, we solve the first problem modifying the GHT to deal with an inexact matching problem. In this paper, we show how this idea can be efficiently and accurately realized. Experimental results are shown with two different databases of real, unsegmented images.