Neural network-based shape retrieval using moment invariants and Zernike moments.

Shape is one of the fundamental image features for use in Content-Based Image Retrieval (CBIR). Compared with other visual features such as color and texture, it is extremely powerful and provides capability for object recognition and similarity-based image retrieval. In this thesis, we propose a Neural Network-Based Shape Retrieval System using Moment Invariants and Zernike Moments. Moment Invariants and Zernike Moments are two region-based shape representation schemes and are derived from the shape in an image and serve as image features. k means clustering is used to group similar images in an image collection into k clusters whereas Neural Network is used to facilitate retrieval against a given query image. Neural Network is trained by the clustering result on all of the images in the collection using back-propagation algorithm. In this scheme, Neural Network serves as a classifier such that moments are inputs to the Neural Network and the output is one of the k classes that have the largest similarities to the query image. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .C444. Source: Masters Abstracts International, Volume: 44-03, page: 1396. Thesis (M.Sc.)--University of Windsor (Canada), 2005

Color Image Clustering using Block Truncation Algorithm

With the advancement in image capturing device, the image data been generated at high volume. If images are analyzed properly, they can reveal useful information to the human users. Content based image retrieval address the problem of retrieving images relevant to the user needs from image databases on the basis of low-level visual features that can be derived from the images. Grouping images into meaningful categories to reveal useful information is a challenging and important problem. Clustering is a data mining technique to group a set of unsupervised data based on the conceptual clustering principal: maximizing the intraclass similarity and minimizing the interclass similarity. Proposed framework focuses on color as feature. Color Moment and Block Truncation Coding (BTC) are used to extract features for image dataset. Experimental study using K-Means clustering algorithm is conducted to group the image dataset into various clusters

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

Feature Selection for Image Retrieval based on Genetic Algorithm

This paper describes the development and implementation of feature selection for content based image retrieval. We are working on CBIR system with new efficient technique. In this system, we use multi feature extraction such as colour, texture and shape. The three techniques are used for feature extraction such as colour moment, gray level co- occurrence matrix and edge histogram descriptor. To reduce curse of dimensionality and find best optimal features from feature set using feature selection based on genetic algorithm. These features are divided into similar image classes using clustering for fast retrieval and improve the execution time. Clustering technique is done by k-means algorithm. The experimental result shows feature selection using GA reduces the time for retrieval and also increases the retrieval precision, thus it gives better and faster results as compared to normal image retrieval system. The result also shows precision and recall of proposed approach compared to previous approach for each image class. The CBIR system is more efficient and better performs using feature selection based on Genetic Algorithm

Indexing and Retrieving Photographic Images Using a Combination of Geo-Location and Content-Based Features

This paper presents a novel method that automatically indexes searches for relevant images using a combination of geo-coded information and content-based visual features. Photographic images are labeled with their corresponding GPS (Global Positioning System) coordinates and UTC time (Coordinated Universal Time) information at the moment of capture, which are then utilized to create spatial and temporal indexes for photograph retrieval. Assessing the performance in terms of average precision and F-score with real-world image collections revealed that the proposed approach significantly improved and enhanced the retrieval process compared to searches based on visual content alone. Combining content and context information thus offers a useful and meaningful new approach to searching and managing large image collections

An efficient image retrieval scheme for colour enhancement of embedded and distributed surveillance images

From the past few years, the size of the data grows exponentially with respect to volume, velocity, and dimensionality due to wide spread use of embedded and distributed surveillance cameras for security reasons. In this paper, we have proposed an integrated approach for biometric-based image retrieval and processing which addresses the two issues. The first issue is related to the poor visibility of the images produced by the embedded and distributed surveillance cameras, and the second issue is concerned with the effective image retrieval based on the user query. This paper addresses the first issue by proposing an integrated image enhancement approach based on contrast enhancement and colour balancing methods. The contrast enhancement method is used to improve the contrast, while the colour balancing method helps to achieve a balanced colour. Importantly, in the colour balancing method, a new process for colour cast adjustment is introduced which relies on statistical calculation. It adjusts the colour cast and maintains the luminance of the image. The integrated image enhancement approach is applied to the enhancement of low quality images produced by surveillance cameras. The paper addresses the second issue relating to image retrieval by proposing a content-based image retrieval approach. The approach is based on the three features extraction methods namely colour, texture and shape. Colour histogram is used to extract the colour features of an image. Gabor filter is used to extract the texture features and the moment invariant is used to extract the shape features of an image. The use of these three algorithms ensures that the proposed image retrieval approach produces results which are highly relevant to the content of an image query, by taking into account the three distinct features of the image and the similarity metrics based on Euclidean measure. In order to retrieve the most relevant images, the proposed approach also employs a set of fuzzy heuristics to improve the quality of the results further. The result

Content-based indexing of low resolution documents

In any multimedia presentation, the trend for attendees taking pictures of slides that interest them during the presentation using capturing devices is gaining popularity. To enhance the image usefulness, the images captured could be linked to image or video database. The database can be used for the purpose of file archiving, teaching and learning, research and knowledge management, which concern image search. However, the above-mentioned devices include cameras or mobiles phones have low resolution resulted from poor lighting and noise. Content-Based Image Retrieval (CBIR) is considered among the most interesting and promising fields as far as image search is concerned. Image search is related with finding images that are similar for the known query image found in a given image database. This thesis concerns with the methods used for the purpose of identifying documents that are captured using image capturing devices. In addition, the thesis also concerns with a technique that can be used to retrieve images from an indexed image database. Both concerns above apply digital image processing technique. To build an indexed structure for fast and high quality content-based retrieval of an image, some existing representative signatures and the key indexes used have been revised. The retrieval performance is very much relying on how the indexing is done. The retrieval approaches that are currently in existence including making use of shape, colour and texture features. Putting into consideration these features relative to individual databases, the majority of retrievals approaches have poor results on low resolution documents, consuming a lot of time and in the some cases, for the given query image, irrelevant images are obtained. The proposed identification and indexing method in the thesis uses a Visual Signature (VS). VS consists of the captures slides textual layout’s graphical information, shape’s moment and spatial distribution of colour. This approach, which is signature-based are considered for fast and efficient matching to fulfil the needs of real-time applications. The approach also has the capability to overcome the problem low resolution document such as noisy image, the environment’s varying lighting conditions and complex backgrounds. We present hierarchy indexing techniques, whose foundation are tree and clustering. K-means clustering are used for visual features like colour since their spatial distribution give a good image’s global information. Tree indexing for extracted layout and shape features are structured hierarchically and Euclidean distance is used to get similarity image for CBIR. The assessment of the proposed indexing scheme is conducted based on recall and precision, a standard CBIR retrieval performance evaluation. We develop CBIR system and conduct various retrieval experiments with the fundamental aim of comparing the accuracy during image retrieval. A new algorithm that can be used with integrated visual signatures, especially in late fusion query was introduced. The algorithm has the capability of reducing any shortcoming associated with normalisation in initial fusion technique. Slides from conferences, lectures and meetings presentation are used for comparing the proposed technique’s performances with that of the existing approaches with the help of real data. This finding of the thesis presents exciting possibilities as the CBIR systems is able to produce high quality result even for a query, which uses low resolution documents. In the future, the utilization of multimodal signatures, relevance feedback and artificial intelligence technique are recommended to be used in CBIR system to further enhance the performance

Region of interest and color moment method for freshwater fish identification

One of the important features in content based image retrieval is color feature. The color feature is the most widely used visual features. Extracting feature image depends on the problem to identify the region or object of interest that is complex in content. This paper presents a methodology to recognize certain freshwater images using region of interest and color feature. In this work, we have considered 7 varieties of freshwater fish, Gourami, Mas/Common carper, Mas Orange, Mas Kancra, Mujair/Java Tilapia, Nila/Nile Tilapia, and Patin. Each variety consists of 20 images. We deployed Color Moment Feature after Region of Interest process to extract the feature. Euclid is used for recognition. Considering only a feature, the classification accuracy of 89% is obtained using color moment. The research technique shows promise for eventually being able to do so, and for the future will help to get important information from the image

Cuckoo Search Algorithm Based Feature Selection in Image Retrieval System

Efficiency an d retrieval time are very important issues in any content-based image retrieval system. In this study, an efficient image retrieval system was introduced depending on several features extracted from the database images, namely color moment (Mean, Standard Deviation), GLCM, and DWT( only LL-sub band). To increase the retrieval speed, Cuckoo search algorithm was used to select the important positions that contain full power features from the (LL-sub band). On using the Cuckoo search algorithm, only (50) important positions were chosen out of the total (24576) positions within (LL- sub band). These positions were stored for later use when entering a query image. Thus, the time taken to retrieve images was greatly reduced and this process also increased the efficiency of the system due to the fact that the selected positions gave the lowest distance measures between the query images and the similar images when evaluated using Manhattan distance measure. Two effectual performance measures (precision &amp; recall) were used to calculate the accuracy of the system. The findings proved the system efficiency when compared to other previous works. Keywords: CBIR, Color Moment, GLCM, DWT, Cuckoo Search algorithm, Manhattan measure DOI: 10.7176/JEP/10-15-08 Publication date:May 31st 201