Recognition of clothing categories from videos is appealing to emerging applications such as intelligent customer profile analysis and computer-aided fashion design. This paper presents a complete system to tag clothing categories in real-time, which addresses some practical complications in surveillance videos. Specifically, we take advantage of face detection and tracking to locate human figures and develop an efficient clothing segmentation method utilizing Voronoi images to select seeds for region growing. We compare clothing representations combining color histograms and 3 different texture descriptors. Evaluated on a video dataset with 937 persons and 25441 cloth instances, the system demonstrates promising results in recognizing 8 clothing categories.

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Real-time clothing recognition in surveillance videos

The effective content-based image retrieval (CBIR) needs efficient extraction of low level features like color, texture and shapes for indexing and fast query image matching with indexed images for the retrieval of similar images. Features are extracted from images in pixel and compressed domains. However, now most of the existing images are in compressed formats like JPEG using DCT (discrete cosine transformation). In this paper we study the issues of efficient extraction of features and the effective matching of images in the compressed domain. In our method the quantized histogram statistical texture features are extracted from the DCT blocks of the image using the significant energy of the DC and the first three AC coefficients of the blocks. For the effective matching of the image with images, various distance metrics are used to measure similarities using texture features. The analysis of the effective CBIR is performed on the basis of various distance metrics in different number of quantization bins. The proposed method is tested by using Corel image database and the experimental results show that our method has robust image retrieval for various distance metrics with different histogram quantization in a compressed domain.

Original Article: Analysis of distance metrics in content-based image retrieval using statistical quantized histogram texture features in the DCT domain

Face is the index of mind. It is a complex multidimensional structure and needs a good computing technique for recognition. While using automatic system for face recognition, computers are easily confused by changes in illumination, variation in poses and change in angles of faces. A numerous techniques are being used for security and authentication purposes which includes areas in detective agencies and military purpose. These surveys give the existing methods in automatic face recognition and formulate the way to still increase the performance.

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Face Recognition Techniques: A Survey

Face detection is one of the most relevant applications of image processing and biometric systems. Artificial neural networks (ANN) have been used in the field of image processing and pattern recognition. There is lack of literature surveys which give overview about the studies and researches related to the using of ANN in face detection. Therefore, this research includes a general review of face detection studies and systems which based on different ANN approaches and algorithms. The strengths and limitations of these literature studies and systems were included also.

Review of Face Detection Systems Based Artificial Neural Networks Algorithms

Several CBIR schemes have been devised by the formation of the low dimensional image feature vector from the primitive visual features such as color, texture and/or shape of the image to speed up the retrieval process. In this paper, the visual contents of the images have been extracted using block level Discrete Cosine Transformation (DCT) and Gray Level Co-occurrence Matrix (GLCM). Since the DC coefficients based feature vector has retained the most significant visual components of the image, so initially, we have computed DC coefficients based uniform quantized histogram and some statistical parameters are derived from that histogram for the formation of the DC feature vector. Subsequently, other significant visual features are computed from the residual image where the residual image is obtained by taking the difference between the original image plane and the DC coefficients based reconstructed image plane. Thereafter, some statistical parameters from GLCMs of the residual image are considered for the construction of the GLCM based feature vector. This feature vector is suitable to identify the texture features of the residual image in a more effective way. The single feature vector has been obtained by combining DC and GLCM feature vectors since the combined extracted features from the images increase the accuracy of any image retrieval system. We have tested the scheme either in intensity image or on three color planes of an RGB color image. The experimental results are evaluated on two standard image databases and demonstrate the effectiveness of the proposed scheme in terms of average retrieval accuracy. In addition, the overall speed of the proposed CBIR system is high due to the formation of the low dimensional significant feature vector. The comparative results also show that the proposed scheme provides effective accuracy than some other state-of-the-art CBIR schemes.

A novel image retrieval scheme using gray level co-occurrence matrix descriptors of discrete cosine transform based residual image

This paper proposes a robust schema for face detection system via Gaussian mixture model to segment image based on skin color. After skin and non skin face candidatespsila selection, features are extracted directly from discrete cosine transform (DCT) coefficients computed from these candidates. Moreover, the back-propagation neural networks are used to train and classify faces based on DCT feature coefficients in Cb and Cr color spaces. This schema utilizes the skin color information, which is the main feature of face detection. DCT feature values of faces, representing the data set of skin/non-skin face candidates obtained from Gaussian mixture model are fed into the back-propagation neural networks to classify whether the original image includes a face or not. Experimental results shows that the proposed schema is reliable for face detection, and pattern features are detected and classified accurately by the backpropagation neural networks.

Face detection based neural networks using robust skin color segmentation

Face detection is one of the challenging problems in the image processing. A novel face detection system is presented in this paper. The approach relies on skin based color, while features extracted from two dimentional discreate cosine transfer (DCT) and neural networks. which can be used to detect faces by using skin color from DCT coefficient of Cb and Cr feature vectors. This system contains the skin color which is the main feature of faces for detection and then the skin face candidate is examined by using the neural networks, which learns from the feature of faces to classify whether the original image includes a face or not. The processing stage is based on normalization and discreate cosine transfer ( DCT ). Finally the classification based on neural networks approach. The experiments results on upright frontal color face images from the internet show an a excellent detection rate.

Face detection based on skin color in image by neural networks

This paper proposes a new simple method of Discrete Cosine Transform (DCT) feature extraction that is used to accelerate the speed and decrease the storage needed in the image retrieving process. Image features are accessed and extracted directly from JPEG compressed domain. This method extracts and constructs a feature vector of histogram quantization from partial DCT coefficient in order to count the number of coefficients that have the same DCT coefficient over all image blocks. The database image and query image is equally divided into a non overlapping 8X8 block pixel, each of which is associated with a feature vector of histogram quantization derived directly from discrete cosine transform DCT. Users can select any query as the main theme of the query image. The retrieved images are those from the database that bear close resemblance with the query image and the similarity is ranked according to the closest similar measures computed by the Euclidean distance. The experimental results are significant and promising and show that our approach can easily identify main objects while to some extent reducing the influence of background in the image and in this way improves the performance of image retrieval.

An efficient Image Retrieval through DCT Histogram Quantization

This paper proposes an efficient content-based image retrieval scheme based on Semantic Object Detection (SOD). The feature extraction process uses quantized discrete cosine transform (DCT) blocks where the retrieval of the query image is performed using a histogram-based similarity measure. SOD aims to reduce the size of the database from which the retrieval of similar images is conducted. The use of SOD has been shown to improve the retrieval performance and outperform related work in the literature.

Aamer Mohamed

Papers

Face detection based neural networks using robust skin color segmentation

Face detection based on skin color in image by neural networks

An efficient Image Retrieval through DCT Histogram Quantization

Efficient content based image retrieval based on Semantic Object Detection