Sudeep D. Thepade

Performance comparison of feature vector extraction techniques in RGB color space using block truncation coding for content based image classification with discrete classifiers

Content based image classification is a vital component of machine learning and is attaining increasing importance in the field of image processing. This paper has carried out widespread comparison of block truncation coding based techniques for feature vector extraction of images which is a precursor of image classification. A new block truncation coding (BTC) based technique using even and odd image parts for feature vector extraction is also introduced to perform image classification. The performances of classifier algorithms are compared in Receiver Operating Characteristic (ROC) Space. Two different categories of classifiers viz. K Nearest Neighbor (KNN) Classifier and RIDOR Classifier are being used to observe the degree of classification for various techniques under six different feature vector extraction environments.

Performance Boost of Block Truncation Coding based Image Classification using Bit Plane Slicing

Image classification demands major attention with increasing volume of available image data. The paper has shown performance boosting of image classification after associating Bit Plane Slicing with Block Truncation Coding (BTC) for feature extraction. Here more significant bit planes were considered for extraction of feature vectors. RGB color space was considered to carry out the experimentation. A database of 900 images was used for evaluation purpose. KeywordsPlane Slicing, BTC, CBIC, RGB

A Novel Feature Extraction Technique Using Binarization of Bit Planes for Content Based Image Classification

A number of techniques have been proposed earlier for feature extraction using image binarization. Efficiency of the techniques was dependent on proper threshold selection for the binarization method. In this paper, a new feature extraction technique using image binarization has been proposed. The technique has binarized the significant bit planes of an image by selecting local thresholds. The proposed algorithm has been tested on a public dataset and has been compared with existing widely used techniques using binarization for extraction of features. It has been inferred that the proposed method has outclassed all the existing techniques and has shown consistent classification performance.

Color Content based Video Retrieval using Block Truncation Coding with Different Color Spaces

The volume of video data is increasing because of advanced technology in imaging communication. This tends to increase the popularity the videos. The conventional textual description which is based on text entered by user, so, suffers from user perception. Hence, content based video retrieval is more attractive than text based retrieval, as it gives result proximity to semantic view. Here, a novel colour content based video retrieval based on block truncation coding is proposed with the help of seven assorted colour spaces. Video database of 500 video samples spread across 10 categories is considered as a test bed for experimentation. Performance comparison is done with the help of cross-over point of average precision and average recall values of 500 query videos fired on the whole database. Experimental results have shown performance improvement for video retrieval in chromaticityluminance colour spaces as compared conventional colour space (RGB). The best performance is observed by Block Truncation Coding withKekre’s LUV colour space followed by YCbCr colour space. General Terms Content Based Video Retrieval (CBVR), Feature Extraction, Color Spaces, Block Truncation Coding (BTC)

Extended performance comparison of tiling based image compression using wavelet transforms & hybrid wavelet transforms

The storage of images is becoming difficult with number of images growing to millions & billions. Hence the image compression is becoming absolute necessity in computing field. Use of the energy compaction property of orthogonal transforms is exploited for lossy image compression. In the recent research attempts better quality of compression is observed with the use of wavelet transforms & hybrid wavelet transforms. Further tiling based image compression is proven to be better with few hybrid wavelet transforms. The paper presents extended performance comparison of more variations of hybrid wavelet transforms for tiling based image compression. The experimentation is done on set of ten color images with size 256X256 each. Mean Square Error (MSE) between the original color image & compressed color image is employed to compare performance of various hybrid wavelet transforms for ten different assorted compression ratios (10%, 20%....90% & 95%). The hybrid wavelet transform generated using Cosine & Kekre Transforms (Cosine + Kekre) has shown the best performance with minimum MSEs for all compression ratios closely followed by the Hybrid Wavelet Transform generated using Cosine & Haar Transforms.

Image Classification Using Advanced Block Truncation Coding with Ternary Image Maps

Incredible escalation of Information Technology leads to generation, storage and transfer of enormous information. Easy and round the clock access of data has been made possible by virtue of world wide web. The high capacity storage devices and communication links facilitates the archiving of information in the form of multimedia. This type of information comprises of images in majority and is growing in number by leaps and bounds. But the usefulness of this information will be at stake if maximum information is not retrieved in minimum time. The huge database of information comprising of multiple number of image data is diversified mix in nature. Proper Classification of Image data based on their content is highly applicable in these databases to form limited number of major categories. The novel ternary block truncation coding (Ternary BTC) is proposed in the paper, also the comparison of Binary block truncation coding (Binary BTC) and Ternary Block Truncation Coding is done for image classification. Here two image databases are considered for experimentation. The proposed ternary BTC is found to be better than Binary BTC for image classification as indicated by higher average success rate.

Image Classification using Block Truncation Coding with Assorted Color Spaces

The paper portrays comprehensive performance comparison of image classification techniques using block truncation coding (BTC) with assorted color spaces. Overall six color spaces have been explored which includes RGB color space for applying BTC to figure out the feature vector in Content Based Image Classification (CBIC) techniques. A generic database with 900 images having 100 images per category spread across 9 different categories have been considered to conduct the experimentation with the proposed Image Classification technique. On the whole nine hundred queries have been fired. The average success rate of class determination for each of the color spaces has been computed and considered for performance analysis. The results explicitly reveal performance improvement (higher average success rate values) with proposed colorBTC methods with luminance chromaticity color spaces compared to RGB color space. Best result is shown by YUV color space based BTC in content based image classification.

Novel visual content summarization in videos using keyframe extraction with Thepade's Sorted Ternary Block truncation Coding and Assorted similarity measures

A video is made up of frames. Generally few video processing applications demand to process each video frame one by one, but processing each frame consumes lot of video content summarization helps in improvising the processing speed for such applications. Key frames in video are considered for content summarization. Key frame is a frame in which there is a major change as compared to the previous video frames. Hence key frame extraction becomes very important in Video Content Summarization. In applications needing content summarization, like data storage, retrieval and surveillance, key frames extraction plays a vital role. The Block truncation Coding is one of color feature extraction methods in Content Based Video Retrieval (CBVR). The extended version of BTC is Thepades Sorted Ternary BTC (TSTBTC). This paper explains key frame extraction using Assorted similarity measure and TSTBTC.

Feature Extraction with Ordered Mean Values for Content Based Image Classification

Categorization of images into meaningful classes by efficient extraction of feature vectors from image datasets has been dependent on feature selection techniques. Traditionally, feature vector extraction has been carried out using different methods of image binarization done with selection of global, local, or mean threshold. This paper has proposed a novel technique for feature extraction based on ordered mean values. The proposed technique was combined with feature extraction using discrete sine transform (DST) for better classification results using multitechnique fusion. The novel methodology was compared to the traditional techniques used for feature extraction for content based image classification. Three benchmark datasets, namely, Wang dataset, Oliva and Torralba (OT-Scene) dataset, and Caltech dataset, were used for evaluation purpose. Performance measure after evaluation has evidently revealed the superiority of the proposed fusion technique with ordered mean values and discrete sine transform over the popular approaches of single view feature extraction methodologies for classification.

Image Compression using Cosine - Slant Hybrid Wavelet Transform with Assorted Color Spaces

Gigantic size of multimedia data and images being generated stored and transmitted across the digital web today, which has raised new research concerns in computing field. One of such concerns is compression of such data with minimal quality distortion. In recent work, the hybrid wavelet transforms (HWT) are proven to be better in image compression as compared to the constituent transforms considered individually [1, 2]. Then further the proportion wise performance comparison of the constituent transforms considered for generation of HWT is also studied [3, 4]. Here the paper presents the performance appraise of various color spaces along with the conventional RGB color space in image compression using cosine-slant HWT with varying proportions of constituent cosine and slant transforms. The experimentation is done on a test-bed having 15 images of various sizes and varied compression ratios. The results show that LUV color space with 1:4 proportion of cosine-slant transform in HWT gives better quality of compression for higher compression ratios (90% and 95%). For 85% compression ratio, LUV color space with 1:1 proportion of cosine-slant transforms in HWT gives better compression quality. While in lower compression ratios (60% to 80%), the RGB color space gives better compression with 1:1 proportion of cosine-slant transforms in HWT. General Terms Image Compression.