Image classification using adapted codebook

The Bag of Visual Words (BoW) model is one of the most popular and effective image classification frameworks in the recent literature. The optimal formation of a visual vocabulary remains unclear, and the size of the vocabulary also affects the performance of image classification. Empirically, larger vocabulary leads to higher classification accuracy. However, larger vocabulary needs more memory and intensive computational resources. In this paper, we propose a multiresolution feature coding (MFC) framework via aggregating feature codings obtained from a set of small visual vocabularies with different sizes, where each vocabulary is obtained by a clustering algorithm, and different clustering algorithm discovers different aspect of image features. In MFC, feature codings from different visual vocabularies are aggregated adaptively by a modified Online Passive‐Aggressive Algorithm under the histogram intersection kernel, which lead to a closed‐form solution. Experiments demonstrate that the proposed method (1) obtains the same if not higher classification accuracy than the BoW model with a large visual vocabulary; and (2) needs much less memory and computational resources.

Locality-constrained affine subspace coding for image classification and retrieval

The bag of visual words (BOW) model is an efficient image representation technique for image categorization and annotation tasks. Building good visual vocabularies, from automatically extracted image feature vectors, produces discriminative visual words, which can improve the accuracy of image categorization tasks. Most approaches that use the BOW model in categorizing images ignore useful information that can be obtained from image classes to build visual vocabularies. Moreover, most BOW models use intensity features extracted from local regions and disregard colour information, which is an important characteristic of any natural scene image. In this paper, we show that integrating visual vocabularies generated from each image category improves the BOW image representation and improves accuracy in natural scene image classification. We use a keypoint density-based weighting method to combine the BOW representation with image colour information on a spatial pyramid layout. In addition, we show that visual vocabularies generated from training images of one scene image dataset can plausibly represent another scene image dataset on the same domain. This helps in reducing time and effort needed to build new visual vocabularies. The proposed approach is evaluated over three well-known scene classification datasets with 6, 8 and 15 scene categories, respectively, using 10-fold cross-validation. The experimental results, using support vector machines with histogram intersection kernel, show that the proposed approach outperforms baseline methods such as Gist features, rgbSIFT features and different configurations of the BOW model.

With the advancement in multimedia technologies, the video databases are exponentially increasing in size which are creating many challenges for efficient indexing and retrieval of the videos. Interactive and efficient search engines allow users to query some part of the videos or search some particular scenes in the video databases. Segmentation and retrieval of videos scenes are getting popular which make video indexing more flexible and efficient. In this paper, we automatically segment the videos into scenes (visually related video shots). We first segment the videos into shots and then merge the shots which are visually similar. We represent shots by bag of visual words (BoVW) model and compute the similarity of shots with each other within the sliding window of length L, sliding window makes similarity computation efficient as the similarity of each shot is computed with its L neighbors only, instead of whole pool of shots. Experiments on cinematic videos and dramas show the effectiveness of proposed technique.

At present, the Bag of Visual Words (BoVW) model has been successfully applied to Image Retrieval and Object Recognition. However, how to build a visual dictionary with high efficiency and low redundancy is still a key issue. Therefore, in this paper, we proposed an improved BoVW model to study the dorsal hand vein recognition problem. Specifically, when constructing a visual dictionary, we first use K-means++ to obtain some clustering center points for each image category, and each center point represents a visual word. Secondly, we combine all the categories of words into a visual dictionary. Finally, we use the mutual information method to eliminate redundancy between words to optimize the visual dictionary. The proposed method was tested on image databases collected under weak constraints, and the results show that the improved model has good robustness, low computational complexity, and the expression of each image category is more prominent, so it can get better performance.

The bag of visual words (BoVW) model is a powerful tool for feature representation that can integrate various handcrafted features like intensity, texture, and spatial information. In this paper, we propose a novel BoVW-based method that incorporates texture and spatial information for the content-based image retrieval to assist radiologists in clinical diagnosis. This paper presents a texture-specific BoVW method to represent focal liver lesions (FLLs). Pixels in the region of interest (ROI) are classified into nine texture categories using the rotation-invariant uniform local binary pattern method. The BoVW-based features are calculated for each texture category. In addition, a spatial cone matching (SCM)-based representation strategy is proposed to describe the spatial information of the visual words in the ROI. In a pilot study, eight radiologists with different clinical experience performed diagnoses for 20 cases with and without the top six retrieved results. A total of 132 multiphase computed tomography volumes including five pathological types were collected. The texture-specific BoVW was compared to other BoVW-based methods using the constructed dataset of FLLs. The results show that our proposed model outperforms the other three BoVW methods in discriminating different lesions. The SCM method, which adds spatial information to the orderless BoVW model, impacted the retrieval performance. In the pilot trial, the average diagnosis accuracy of the radiologists was improved from 66 to 80% using the retrieval system. The preliminary results indicate that the texture-specific features and the SCM-based BoVW features can effectively characterize various liver lesions. The retrieval system has the potential to improve the diagnostic accuracy and the confidence of the radiologists.

Today, image classification is considered as one of the most important and challenging tasks in computer vision. This paper presents a new method for image classification using Bag Of Visual Words and Local Binary Patterns (LBP). The bag-of-visual-words (BoVW) model has been proven to be very efficient for image classification and image retrieval. However, most proposals directly use local features extracted from an image while ignoring hidden information that could be extracted from an image. To solve this problem, we propose a novel image classification method using information extracted from different channels of the image and the grayscale version of the image. In this way more discriminant information is extracted from the image and as a result the constructed BoVW model gives highly discriminative features that considerably increases the classification performance. In this work we embed features extracted using LBP into BoVW model to construct our proposed scene classification model. The choice of LBP as image feature descriptor is because of the fact that the content of most of the scene images contains textural information so extracting LBP features is a very wise choice compared to other popular image features like Scale Invariant Feature Transform (SIFT) that fails to capture image information in homogeneous areas or textual images. Experiments on Oliva and Torralba (OT) dataset demonstrate the effectiveness of the proposed method.

Object classification has been one of the main tasks in computer vision. With the fast development of deep learning, its performance in image classification and object recognition has presented dramatic improvements. In this paper, we propose a new deep convolutional neural network (CNN) architecture for robust object classification. The proposed model is fused with three traditional CNN approaches, Densenet201, Resnet50, and our proposed residual CNN. The fused network architecture allows parallel processing of the multiple networks for keeping the system sped up. A single shot deep convolution network is trained as an object detector to generate all possible candidates of different object classes. The output of each neural network is representing a single vote that is used in the classification process. 3-to-1 voting criteria are applied in the final classification decision between the candidate object classes. Several experiments were conducted to evaluate the performance of the proposed network. The experimental results show that the proposed approach has better performance than the networks used in the fusion process when they act individually. It also has lower miss rates when compared to several states of art methodologies.

Image classification is getting more attention in the area of computer vision. During the past few years, a lot of research has been done on image classification using classical machine learning and deep learning techniques. Presently, deep learning-based techniques have given stupendous results. The performance of a classification system depends on the quality of features extracted from an image. The better is the quality of extracted features, the more the accuracy will be. Although, numerous deep learning-based methods have shown enormous performance in image classification, still due to various challenges deep learning methods are not able to extract all the important information from the image. This results in a reduction in overall classification accuracy. The goal of the present research is to improve the image classification performance by combining the deep features extracted using popular deep convolutional neural network, VGG19, and various handcrafted feature extraction methods, i.e., SIFT, SURF, ORB, and Shi-Tomasi corner detector algorithm. Further, the extracted features from these methods are classified using various machine learning classification methods, i.e., Gaussian Naïve Bayes, Decision Tree, Random Forest, and eXtreme Gradient Boosting (XGBClassifier) classifier. The experiment is carried out on a benchmark dataset Caltech-101. The experimental results indicate that Random Forest using the combined features give 93.73% accuracy and outperforms other classifiers and methods proposed by other authors. The paper concludes that a single feature extractor whether shallow or deep is not enough to achieve satisfactory results. So, a combined approach using deep learning features and traditional handcrafted features is better for image classification.

A popular approach to problems in image classification is to represent the image as a bag of visual words and then employ a classifier to categorize the image. Unfortunately, a significant shortcoming of this approach is that the clustering and classification are disconnected. Since the clustering into visual words is unsupervised, the representation does not necessarily capture the aspects of the data that are most useful for classification. More seriously, the semantic relationship between clusters is lost, causing the overall classification performance to suffer. We introduce "discriminative cluster refinement" (DCR), a method that explicitly models the pairwise relationships between different visual words by exploiting their co-occurrence information. The assigned class labels are used to identify the co-occurrence patterns that are most informative for object classification. DCR employs a maximum-margin approach to generate an optimal kernel matrix for classification. One important benefit of DCR is that it integrates smoothly into existing bag-of-words information retrieval systems by employing the set of visual words generated by any clustering method. While DCR could improve a broad class of information retrieval systems, this paper focuses on object category recognition. We present a direct comparison with a state-of-the art method on the PASCAL 2006 database and show that cluster refinement results in a significant improvement in classification accuracy given a small number of training examples.

Image classification toward breast cancer using deeply-learned quality features

With the development of deep learning, CNNs have become mainstream in image classification. However, CNN performance is affected by factors like loss function choice and optimization algorithm. This paper aims to improve CNN performance in image classification by optimizing SGD. Firstly, we analyze different loss functions and propose a suitable optimization strategy. Second, we explore SGD and its variants' roles in CNN training, focusing on accelerating convergence and improving accuracy. Experiments show the proposed scheme effectively enhances CNN performance, providing a reliable solution for image classification. This study supports CNN applications in image recognition and lays a foundation for exploring more efficient optimization methods.

This research paper presents an advanced deep learning framework for tomato ripeness and maturity detection, employing Convolutional Neural Networks (CNNs) as the primary tool for automated classification. In particular, the study emphasizes the utilization of the ResNet-152 architecture, a sophisticated deep neural network known for its exceptional performance in image classification tasks. ResNet-152 addresses the challenges of fine-grained categorization by enabling the extraction of intricate visual features such as color nuances, textures, and shapes inherent to tomatoes. This eliminates the necessity for manual feature engineering, enhancing the model's ability to discern between "RIPE," "UNRIPE," and "ROTTEN" tomato classes with unprecedented accuracy. The ResNet-152 architecture's effectiveness lies in its unique design, featuring residual blocks that facilitate the training of very deep networks. This architecture mitigates the vanishing gradient problem and enables the model to learn complex hierarchical features, contributing to its state-of-the-art performance in image classification. In the context of tomato ripeness detection, ResNet-152 acts as a powerful tool for capturing and understanding the intricate visual cues that define different ripeness states, laying the foundation for an efficient and accurate automated sorting system in the agricultural and food processing industries.

With the development of the Internet of Things, the application of computer vision on mobile phones is becoming more and more extensive and people have higher and higher requirements for the timeliness of the recognition results returned and the processing capabilities of the mobile phone for image recognition. However, the processing capability and storage capability of the user terminal equipment cannot meet the needs of identifying and storing a large number of pictures, and the data transmission process will cause high energy consumption of the terminal equipment. At the same time, multisource deep transfer learning has outstanding performance in computer vision and image classification. However, due to the huge amount of calculation of the deep network model, it is impossible to use the existing excellent network model to realize image recognition and classification on the mobile terminal. In order to solve the abovementioned problems, we propose a multisource mobile transfer learning algorithm based on dynamic model compression, this algorithm considers the realization of multisource transfer learning computing in the case of multiple mobile device computing source domains, and the method also guarantees data privacy and security for each device (origin domain). Meanwhile, extensive experiments show that our method can achieve remarkable results in popular image classification datasets.

플리커, 페이스북과 같은 대용량 소셜 미디어 공유 사이트의 발전으로 이미지 정보가 매우 빠르게 증가하고 있다. 이에 따라 소셜 이미지를 정확하게 검색하기 위한 다양한 연구가 활발히 진행되고 있다. 이미지 태그들의 의미적 연관성을 이용하여 태그기반의 이미지 검색의 정확도를 높이고자 하는 연구를 비롯하여 이미지 단어집(Bag of Visual Words)을 기반으로 웹 이미지를 분류하는 연구도 다양하게 진행되고 있다. 본 논문에서는 이미지에서 배경과 같은 중요도가 떨어지는 정보를 제거하여 중요부분을 찾는 GBVS(Graph Based Visual Saliency)모델을 기존 연구에 사용할 것을 제안한다. 제안하는 방법은 첫 번째, 이미지 태그들의 의미적 연관성을 이용해 1차 분류된 데이터베이스에 SIFT알고리즘을 사용하여 이미지 단어집(BoVW)을 만든다. 두 번째, 테스트할 이미지에 GBVS를 통해서 이미지의 관심영역을 선택하여 테스트한다. 의미연관성 태그와 SIFT기반의 이미지 단어집을 사용한 기존의 방법에 GBVS를 적용한 결과 더 높은 정확도를 보임을 확인하였다. As social multimedia sites are getting popular such as Flickr and Facebook, the amount of image information has been increasing very fast. So there have been many studies for accurate social image retrieval. Some of them were web image classification using semantic relations of image tags and BoVW(Bag of Visual Words). In this paper, we propose a method to detect salient region in images using GBVS(Graph Based Visual Saliency) model which can eliminate less important region like a background. First, We construct BoVW based on SIFT algorithm from the database of the preliminary retrieved images with semantically related tags. Second, detect salient region in test images using GBVS model. The result of image classification showed higher accuracy than the previous research. Therefore we expect that our method can classify a variety of images more accurately.